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Sample records for fuzzy clustering method

  1. Fuzzy Clustering Methods and their Application to Fuzzy Modeling

    DEFF Research Database (Denmark)

    Kroszynski, Uri; Zhou, Jianjun

    1999-01-01

    Fuzzy modeling techniques based upon the analysis of measured input/output data sets result in a set of rules that allow to predict system outputs from given inputs. Fuzzy clustering methods for system modeling and identification result in relatively small rule-bases, allowing fast, yet accurate...... prediction of outputs. This article presents an overview of some of the most popular clustering methods, namely Fuzzy Cluster-Means (FCM) and its generalizations to Fuzzy C-Lines and Elliptotypes. The algorithms for computing cluster centers and principal directions from a training data-set are described....... A method to obtain an optimized number of clusters is outlined. Based upon the cluster's characteristics, a behavioural model is formulated in terms of a rule-base and an inference engine. The article reviews several variants for the model formulation. Some limitations of the methods are listed...

  2. Fuzzy Clustering - Principles, Methods and Examples

    DEFF Research Database (Denmark)

    Kroszynski, Uri; Zhou, Jianjun

    1998-01-01

    One of the most remarkable advances in the field of identification and control of systems -in particular mechanical systems- whose behaviour can not be described by means of the usual mathematical models, has been achieved by the application of methods of fuzzy theory.In the framework of a study...... about identification of "black-box" properties by analysis of system input/output data sets, we have prepared an introductory note on the principles and the most popular data classification methods used in fuzzy modeling. This introductory note also includes some examples that illustrate the use...... of the methods. The examples were solved by hand and served as a test bench for exploration of the MATLAB capabilities included in the Fuzzy Control Toolbox. The fuzzy clustering methods described include Fuzzy c-means (FCM), Fuzzy c-lines (FCL) and Fuzzy c-elliptotypes (FCE)....

  3. Kernel method-based fuzzy clustering algorithm

    Institute of Scientific and Technical Information of China (English)

    Wu Zhongdong; Gao Xinbo; Xie Weixin; Yu Jianping

    2005-01-01

    The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.

  4. Fuzzy Clustering Using C-Means Method

    Directory of Open Access Journals (Sweden)

    Georgi Krastev

    2015-05-01

    Full Text Available The cluster analysis of fuzzy clustering according to the fuzzy c-means algorithm has been described in this paper: the problem about the fuzzy clustering has been discussed and the general formal concept of the problem of the fuzzy clustering analysis has been presented. The formulation of the problem has been specified and the algorithm for solving it has been described.

  5. Fuzzy Clustering Method for Web User Based on Pages Classification

    Institute of Scientific and Technical Information of China (English)

    ZHAN Li-qiang; LIU Da-xin

    2004-01-01

    A new method for Web users fuzzy clustering based on analysis of user interest characteristic is proposed in this article.The method first defines page fuzzy categories according to the links on the index page of the site, then computes fuzzy degree of cross page through aggregating on data of Web log.After that, by using fuzzy comprehensive evaluation method, the method constructs user interest vectors according to page viewing times and frequency of hits, and derives the fuzzy similarity matrix from the interest vectors for the Web users.Finally, it gets the clustering result through the fuzzy clustering method.The experimental results show the effectiveness of the method.

  6. Fuzzy Clustering

    DEFF Research Database (Denmark)

    Berks, G.; Keyserlingk, Diedrich Graf von; Jantzen, Jan

    2000-01-01

    A symptom is a condition indicating the presence of a disease, especially, when regarded as an aid in diagnosis.Symptoms are the smallest units indicating the existence of a disease. A syndrome on the other hand is an aggregate, set or cluster of concurrent symptoms which together indicate...... and clustering are the basic concerns in medicine. Classification depends on definitions of the classes and their required degree of participant of the elements in the cases' symptoms. In medicine imprecise conditions are the rule and therefore fuzzy methods are much more suitable than crisp ones. Fuzzy c......-mean clustering is an easy and well improved tool, which has been applied in many medical fields. We used c-mean fuzzy clustering after feature extraction from an aphasia database. Factor analysis was applied on a correlation matrix of 26 symptoms of language disorders and led to five factors. The factors...

  7. A dynamic fuzzy clustering method based on genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHENG Yan; ZHOU Chunguang; LIANG Yanchun; GUO Dongwei

    2003-01-01

    A dynamic fuzzy clustering method is presented based on the genetic algorithm. By calculating the fuzzy dissimilarity between samples the essential associations among samples are modeled factually. The fuzzy dissimilarity between two samples is mapped into their Euclidean distance, that is, the high dimensional samples are mapped into the two-dimensional plane. The mapping is optimized globally by the genetic algorithm, which adjusts the coordinates of each sample, and thus the Euclidean distance, to approximate to the fuzzy dissimilarity between samples gradually. A key advantage of the proposed method is that the clustering is independent of the space distribution of input samples, which improves the flexibility and visualization. This method possesses characteristics of a faster convergence rate and more exact clustering than some typical clustering algorithms. Simulated experiments show the feasibility and availability of the proposed method.

  8. Improved fuzzy identification method based on Hough transformation and fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    刘福才; 路平立; 潘江华; 裴润

    2004-01-01

    This paper presents an approach that is useful for the identification of a fuzzy model in SISO system. The initial values of cluster centers are identified by the Hough transformation, which considers the linearity and continuity of given input-output data, respectively. For the premise parts parameters identification, we use fuzzy-C-means clustering method. The consequent parameters are identified based on recursive least square. This method not only makes approximation more accurate, but also let computation be simpler and the procedure is realized more easily. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.

  9. Analysis of protein profiles using fuzzy clustering methods

    DEFF Research Database (Denmark)

    Karemore, Gopal Raghunath; Ukendt, Sujatha; Rai, Lavanya

    clustering methods for their classification followed by various validation  measures.    The  clustering  algorithms  used  for  the  study  were  K-  means,  K- medoid, Fuzzy C-means, Gustafson-Kessel, and Gath-Geva.  The results presented in this study  conclude  that  the  protein  profiles  of  tissue......  samples  recorded  by  using  the  HPLC- LIF  system  and  the  data  analyzed  by  clustering  algorithms  quite  successfully  classifies them as belonging from normal and malignant conditions....

  10. Extended Fuzzy Clustering Algorithms

    NARCIS (Netherlands)

    U. Kaymak (Uzay); M. Setnes

    2000-01-01

    textabstractFuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has been applied successfully in various fields including finance and marketing. Despite the successful applications, there are a number of issues that must be dealt with in practical applications of fuz

  11. Classification of excessive domestic water consumption using Fuzzy Clustering Method

    Science.gov (United States)

    Zairi Zaidi, A.; Rasmani, Khairul A.

    2016-08-01

    Demand for clean and treated water is increasing all over the world. Therefore it is crucial to conserve water for better use and to avoid unnecessary, excessive consumption or wastage of this natural resource. Classification of excessive domestic water consumption is a difficult task due to the complexity in determining the amount of water usage per activity, especially as the data is known to vary between individuals. In this study, classification of excessive domestic water consumption is carried out using a well-known Fuzzy C-Means (FCM) clustering algorithm. Consumer data containing information on daily, weekly and monthly domestic water usage was employed for the purpose of classification. Using the same dataset, the result produced by the FCM clustering algorithm is compared with the result obtained from a statistical control chart. The finding of this study demonstrates the potential use of the FCM clustering algorithm for the classification of domestic consumer water consumption data.

  12. Possibilistic Exponential Fuzzy Clustering

    Institute of Scientific and Technical Information of China (English)

    Kiatichai Treerattanapitak; Chuleerat Jaruskulchai

    2013-01-01

    Generally,abnormal points (noise and outliers) cause cluster analysis to produce low accuracy especially in fuzzy clustering.These data not only stay in clusters but also deviate the centroids from their true positions.Traditional fuzzy clustering like Fuzzy C-Means (FCM) always assigns data to all clusters which is not reasonable in some circumstances.By reformulating objective function in exponential equation,the algorithm aggressively selects data into the clusters.However noisy data and outliers cannot be properly handled by clustering process therefore they are forced to be included in a cluster because of a general probabilistic constraint that the sum of the membership degrees across all clusters is one.In order to improve this weakness,possibilistic approach relaxes this condition to improve membership assignment.Nevertheless,possibilistic clustering algorithms generally suffer from coincident clusters because their membership equations ignore the distance to other clusters.Although there are some possibilistic clustering approaches that do not generate coincident clusters,most of them require the right combination of multiple parameters for the algorithms to work.In this paper,we theoretically study Possibilistic Exponential Fuzzy Clustering (PXFCM) that integrates possibilistic approach with exponential fuzzy clustering.PXFCM has only one parameter and not only partitions the data but also filters noisy data or detects them as outliers.The comprehensive experiments show that PXFCM produces high accuracy in both clustering results and outlier detection without generating coincident problems.

  13. Health state evaluation of shield tunnel SHM using fuzzy cluster method

    Science.gov (United States)

    Zhou, Fa; Zhang, Wei; Sun, Ke; Shi, Bin

    2015-04-01

    Shield tunnel SHM is in the path of rapid development currently while massive monitoring data processing and quantitative health grading remain a real challenge, since multiple sensors belonging to different types are employed in SHM system. This paper addressed the fuzzy cluster method based on fuzzy equivalence relationship for the health evaluation of shield tunnel SHM. The method was optimized by exporting the FSV map to automatically generate the threshold value. A new holistic health score(HHS) was proposed and its effectiveness was validated by conducting a pilot test. A case study on Nanjing Yangtze River Tunnel was presented to apply this method. Three types of indicators, namely soil pressure, pore pressure and steel strain, were used to develop the evaluation set U. The clustering results were verified by analyzing the engineering geological conditions; the applicability and validity of the proposed method was also demonstrated. Besides, the advantage of multi-factor evaluation over single-factor model was discussed by using the proposed HHS. This investigation indicated the fuzzy cluster method and HHS is capable of characterizing the fuzziness of tunnel health, and it is beneficial to clarify the tunnel health evaluation uncertainties.

  14. CONSIDERING NEIGHBORHOOD INFORMATION IN IMAGE FUZZY CLUSTERING

    Institute of Scientific and Technical Information of China (English)

    Huang Ning; Zhu Minhui; Zhang Shourong

    2002-01-01

    Fuzzy C-means clustering algorithm is a classical non-supervised classification method.For image classification, fuzzy C-means clustering algorithm makes decisions on a pixel-by-pixel basis and does not take advantage of spatial information, regardless of the pixels' correlation. In this letter, a novel fuzzy C-means clustering algorithm is introduced, which is based on image's neighborhood system. During classification procedure, the novel algorithm regards all pixels'fuzzy membership as a random field. The neighboring pixels' fuzzy membership information is used for the algorithm's iteration procedure. As a result, the algorithm gives a more smooth classification result and cuts down the computation time.

  15. Fuzzy clustering, genetic algorithms and neuro-fuzzy methods compared for hybrid fuzzy-first principles modeling

    NARCIS (Netherlands)

    van Lith, Pascal; van Lith, P.F.; Betlem, Bernardus H.L.; Roffel, B.

    2002-01-01

    Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and

  16. Fuzzy Clustering, Genetic Algorithms and Neuro-Fuzzy Methods Compared for Hybrid Fuzzy-First Principles Modeling

    NARCIS (Netherlands)

    Lith, Pascal F. van; Betlem, Ben H.L.; Roffel, Brian

    2002-01-01

    Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and

  17. Fuzzy Clustering, Genetic Algorithms and Neuro-Fuzzy Methods Compared for Hybrid Fuzzy-First Principles Modeling

    NARCIS (Netherlands)

    Lith, Pascal F. van; Betlem, Ben H.L.; Roffel, Brian

    2002-01-01

    Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and transfe

  18. Fuzzy Clustering, Genetic Algorithms and Neuro-Fuzzy Methods Compared for Hybrid Fuzzy-First Principles Modeling

    NARCIS (Netherlands)

    Lith, Pascal F. van; Betlem, Ben H.L.; Roffel, Brian

    2002-01-01

    Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and transfe

  19. Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

    Science.gov (United States)

    Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

    2014-05-01

    This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach.

  20. A simple and fast method to determine the parameters for fuzzy c-means cluster analysis

    DEFF Research Database (Denmark)

    Schwämmle, Veit; Jensen, Ole Nørregaard

    2010-01-01

    MOTIVATION: Fuzzy c-means clustering is widely used to identify cluster structures in high-dimensional datasets, such as those obtained in DNA microarray and quantitative proteomics experiments. One of its main limitations is the lack of a computationally fast method to set optimal values...... on the main properties of the dataset. Taking the dimension of the set and the number of objects as input values instead of evaluating the entire dataset allows us to propose a functional relationship determining the fuzzifier directly. This result speaks strongly against using a predefined fuzzifier...

  1. Fuzzy Clustering with Novel Separable Criterion

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Fuzzy clustering has been used widely in pattern recognition, image processing, and data analysis. An improved fuzzy clustering algorithm was developed based on the conventional fuzzy c-means (FCM) to obtain better quality clustering results. The update equations for the membership and the cluster center are derived from the alternating optimization algorithm. Two fuzzy scattering matrices in the objective function assure the compactness between data points and cluster centers, and also strengthen the separation between cluster centers in terms of a novel separable criterion. The clustering algorithm properties are shown to be an improvement over the FCM method's properties. Numerical simulations show that the clustering algorithm gives more accurate clustering results than the FCM method.

  2. FINDCLUS : Fuzzy INdividual Differences CLUStering

    NARCIS (Netherlands)

    Giordani, Paolo; Kiers, Henk A. L.

    ADditive CLUStering (ADCLUS) is a tool for overlapping clustering of two-way proximity matrices (objects x objects). In Simple Additive Fuzzy Clustering (SAFC), a variant of ADCLUS is introduced providing a fuzzy partition of the objects, that is the objects belong to the clusters with the so-called

  3. Web Fuzzy Clustering and a Case Study

    Institute of Scientific and Technical Information of China (English)

    LIU Mao-fu; HE Jing; HE Yan-xiang; HU Hui-jun

    2004-01-01

    We combine the web usage mining and fuzzy clustering and give the concept of web fuzzy clustering, and then put forward the web fuzzy clustering processing model which is discussed in detail. Web fuzzy clustering can be used in the web users clustering and web pages clustering. In the end, a case study is given and the result has proved the feasibility of using web fuzzy clustering in web pages clustering.

  4. Neuro-fuzzy system modeling based on automatic fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    Yuangang TANG; Fuchun SUN; Zengqi SUN

    2005-01-01

    A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.

  5. Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation.

    Science.gov (United States)

    Li, Bing Nan; Chui, Chee Kong; Chang, Stephen; Ong, S H

    2011-01-01

    The performance of the level set segmentation is subject to appropriate initialization and optimal configuration of controlling parameters, which require substantial manual intervention. A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy clustering. The controlling parameters of level set evolution are also estimated from the results of fuzzy clustering. Moreover the fuzzy level set algorithm is enhanced with locally regularized evolution. Such improvements facilitate level set manipulation and lead to more robust segmentation. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

  6. The Evaluation of Lane-Changing Behavior in Urban Traffic Stream with Fuzzy Clustering Method

    Directory of Open Access Journals (Sweden)

    Ali Abdi

    2012-11-01

    Full Text Available We present a method for The Evaluation of Lane-Changing Behavior in Urban Traffic Stream with Fuzzy Clustering Method. The trends for drivers Lane-Changing with regard to remarkable effects in traffic are regarded as a major variable in traffic engineering. As a result, various algorithms have presented most models of Lane-Changing developed by means of lane information and the manner of vehicle movement mainly obtained from images process not much attention is given to the characteristics of driver. Lane change divided into two parts the first one are compulsory lane including lane change to turn left or turn right. The second type of change is optional and lane change to improve driving condition. A low speed car is a good example, in this study, through focused group discussion method, drivers information can be obtained so that driver’s personality traits are taken into consideration. Then drivers are divided into four groups by means of Algorithm clusters. The four Algorithms suggest that phase typed cluster is a more suitable method for drivers classification based on Lane-Changing. Through notarization of different type of scenarios of lane change in Iran following results released. The percentage of drivers for each group is 17/5, 35, 20 and 27/ %, respectively.

  7. Fuzzy clustering of mechanisms

    Indian Academy of Sciences (India)

    Amitabha Ghosh; Dilip Kumar Pratihar; M V V Amarnath; Guenter Dittrich; Jorg Mueller

    2012-10-01

    During the course of development of Mechanical Engineering, a large number of mechanisms (that is, linkages to perform various types of tasks) have been conceived and developed. Quite a few atlases and catalogues were prepared by the designers of machines and mechanical systems. However, often it is felt that a clustering technique for handling the list of large number of mechanisms can be very useful,if it is developed based on a scientific principle. In this paper, it has been shown that the concept of fuzzy sets can be conveniently used for this purpose, if an adequate number of properly chosen attributes (also called characteristics) are identified. Using two clustering techniques, the mechanisms have been classified in the present work and in future, it may be extended to develop an expert system, which can automate type synthesis phase of mechanical design. To the best of the authors’ knowledge, this type of clustering of mechanisms has not been attempted before. Thus, this is the first attempt to cluster the mechanisms based on some quantitative measures. It may help the engineers to carry out type synthesis of the mechanisms.

  8. Intuitionistic fuzzy aggregation and clustering

    CERN Document Server

    Xu, Zeshui

    2012-01-01

    This book offers a systematic introduction to the clustering algorithms for intuitionistic fuzzy values, the latest research results in intuitionistic fuzzy aggregation techniques, the extended results in interval-valued intuitionistic fuzzy environments, and their applications in multi-attribute decision making, such as supply chain management, military system performance evaluation, project management, venture capital, information system selection, building materials classification, and operational plan assessment, etc.

  9. Fuzzy clustering with Minkowski distance

    NARCIS (Netherlands)

    P.J.F. Groenen (Patrick); U. Kaymak (Uzay); J.M. van Rosmalen (Joost)

    2006-01-01

    textabstractDistances in the well known fuzzy c-means algorithm of Bezdek (1973) are measured by the squared Euclidean distance. Other distances have been used as well in fuzzy clustering. For example, Jajuga (1991) proposed to use the L_1-distance and Bobrowski and Bezdek (1991) also used the L_inf

  10. Intuitionistic fuzzy hierarchical clustering algorithms

    Institute of Scientific and Technical Information of China (English)

    Xu Zeshui

    2009-01-01

    Intuitionistic fuzzy set (IFS) is a set of 2-tuple arguments, each of which is characterized by a mem-bership degree and a nonmembership degree. The generalized form of IFS is interval-valued intuitionistic fuzzy set (IVIFS), whose components are intervals rather than exact numbers. IFSs and IVIFSs have been found to be very useful to describe vagueness and uncertainty. However, it seems that little attention has been focused on the clus-tering analysis of IFSs and IVIFSs. An intuitionistic fuzzy hierarchical algorithm is introduced for clustering IFSs, which is based on the traditional hierarchical clustering procedure, the intuitionistic fuzzy aggregation operator, and the basic distance measures between IFSs: the Hamming distance, normalized Hamming, weighted Hamming, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance. Subsequently, the algorithm is extended for clustering IVIFSs. Finally the algorithm and its extended form are applied to the classifications of building materials and enterprises respectively.

  11. An Enhanced Level Set Segmentation for Multichannel Images Using Fuzzy Clustering and Lattice Boltzmann Method

    Directory of Open Access Journals (Sweden)

    Savita Agrawal

    2015-11-01

    Full Text Available In the last decades, image segmentation has proved its applicability in various areas like satellite image processing, medical image processing and many more. In the present scenario the researchers tries to develop hybrid image segmentation techniques to generates efficient segmentation. Due to the development of the parallel programming, the lattice Boltzmann method (LBM has attracted much attention as a fast alternative approach for solving partial differential equations. In this project work, first designed an energy functional based on the fuzzy c-means objective function which incorporates the bias field that accounts for the intensity in homogeneity of the real-world image. Using the gradient descent method, corresponding level set equations are obtained from which we deduce a fuzzy external force for the LBM solver based on the model by Zhao. The method is speedy, robust for denoise, and does not dependent on the position of the initial contour, effective in the presence of intensity in homogeneity, highly parallelizable and can detect objects with or without edges. For the implementation of segmentation techniques defined for gray images, most of the time researchers determines single channel segments of the images and superimposes the single channel segment information on color images. This idea leads to provide color image segmentation using single channel segments of multi channel images. Though this method is widely adopted but doesn’t provide complete true segmentation of multichannel ie color images because a color image contains three different channels for Red, green and blue components. Hence segmenting a color image, by having only single channel segments information, will definitely loose important segment regions of color images. To overcome this problem this paper work starts with the development of Enhanced Level Set Segmentation for single channel Images Using Fuzzy Clustering and Lattice Boltzmann Method. For the

  12. An Enhanced Level Set Segmentation for Multichannel Images Using Fuzzy Clustering and Lattice Boltzmann Method

    Directory of Open Access Journals (Sweden)

    Savita Agrawal

    2014-05-01

    Full Text Available In the last decades, image segmentation has proved its applicability in various areas like satellite image processing, medical image processing and many more. In the present scenario the researchers tries to develop hybrid image segmentation techniques to generates efficient segmentation. Due to the development of the parallel programming, the lattice Boltzmann met hod (LBM has attracted much attention as a fast alternative approach for solving partial differential equations. In this project work, first designed an energy functional based on the fuzzy c-means objective function which incorporates the bias field that accounts for the intensity in homogeneity of the real-world image. Using the gradient descent method, corresponding level set equations are obtained from which we deduce a fuzzy external force for the LBM solver based on the model by Zhao. The method is speedy, robust for denoise, and does not dependent on the position of the initial contour, effective in the presence of intensity in homogeneity, highly parallelizable and can detect objects with or without edges. For the implementation of segmentation techniques defined for gr ay images, most of the time researchers determines single channel segments of the images and superimposes the single channel segment information on color images. This idea leads to provide color image segmentation using single channel segments of multi chann el images. Though this method is widely adopted but doesn’t provide complete true segmentation of multichannel ie color images because a color image contains three different channels for Red, green and blue components. Hence segmenting a color image, b y having only single channel segments information, will definitely loose important segment regions of color images. To overcome this problem this paper work starts with the development of Enhanced Level Set Segmentation for single channel Images Using Fuzzy Clustering and Lattice Boltzmann Method. For the

  13. Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms

    Directory of Open Access Journals (Sweden)

    Arindam Chaudhuri

    2015-01-01

    Full Text Available Intuitionistic fuzzy sets (IFSs provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM algorithm to cluster IFSs by hybridizing concepts of FPCM, IFSs, and distance measures. IFPCM resolves inherent problems encountered with information regarding membership values of objects to each cluster by generalizing membership and nonmembership with hesitancy degree. The algorithm is extended for clustering interval valued intuitionistic fuzzy sets (IVIFSs leading to interval valued intuitionistic fuzzy possibilistic C means (IVIFPCM. The clustering algorithm has membership and nonmembership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. The experiments are performed on both real and simulated datasets. It generates valuable information and produces overlapped clusters with different membership degrees. It takes into account inherent uncertainty in information captured by IFSs. Some advantages of algorithms are simplicity, flexibility, and low computational complexity. The algorithm is evaluated through cluster validity measures. The clustering accuracy of algorithm is investigated by classification datasets with labeled patterns. The algorithm maintains appreciable performance compared to other methods in terms of pureness ratio.

  14. Taste Identification of Tea Through a Fuzzy Neural Network Based on Fuzzy C-means Clustering

    Institute of Scientific and Technical Information of China (English)

    ZHENG Yan; ZHOU Chun-guang

    2003-01-01

    In this paper, we present a fuzzy neural network model based on Fuzzy C-Means (FCM) clustering algorithm to realize the taste identification of tea. The proposed method can acquire the fuzzy subset and its membership function in an automatic way with the aid of FCM clustering algorithm. Moreover, we improve the fuzzy weighted inference approach. The proposed model is illustrated with the simulation of taste identification of tea.

  15. COMBINING FUZZY AND CELLULAR LEARNING AUTOMATA METHODS FOR CLUSTERING WIRELESS SENSOR NETWORK TO INCREASE LIFE OF THE NETWORK

    Directory of Open Access Journals (Sweden)

    Javad Aramideh

    2014-11-01

    Full Text Available Wireless sensor networks have attracted attention of researchers considering their abundant applications. One of the important issues in this network is limitation of energy consumption which is directly related to life of the network. One of the main works which have been done recently to confront with this problem is clustering. In this paper, an attempt has been made to present clustering method which performs clustering in two stages. In the first stage, it specifies candidate nodes for being head cluster with fuzzy method and in the next stage, the node of the head cluster is determined among the candidate nodes with cellular learning automata. Advantage of the clustering method is that clustering has been done based on three main parameters of the number of neighbors, energy level of nodes and distance between each node and sink node which results in selection of the best nodes as a candidate head of cluster nodes. Connectivity of network is also evaluated in the second part of head cluster determination. Therefore, more energy will be stored by determining suitable head clusters and creating balanced clusters in the network and consequently, life of the network increases.

  16. Mercer Kernel Based Fuzzy Clustering Self-Adaptive Algorithm

    Institute of Scientific and Technical Information of China (English)

    李侃; 刘玉树

    2004-01-01

    A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self-adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.

  17. Fuzzy Clustering Using the Convex Hull as Geometrical Model

    Directory of Open Access Journals (Sweden)

    Luca Liparulo

    2015-01-01

    Full Text Available A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints imposed by known algorithms using a generalized geometrical model for clusters that is based on the convex hull computation. A method is also proposed in order to determine suitable membership functions and hence to represent fuzzy clusters based on the adopted geometrical model. The convex hull is not only used at the end of clustering analysis for the geometric data interpretation but also used during the fuzzy data partitioning within an online sequential procedure in order to calculate the membership function. Consequently, a pure fuzzy clustering algorithm is obtained where clusters are fitted to the data distribution by means of the fuzzy membership of patterns to each cluster. The numerical results reported in the paper show the validity and the efficacy of the proposed approach with respect to other well-known clustering algorithms.

  18. Clustering Association Rules with Fuzzy Concepts

    Science.gov (United States)

    Steinbrecher, Matthias; Kruse, Rudolf

    Association rules constitute a widely accepted technique to identify frequent patterns inside huge volumes of data. Practitioners prefer the straightforward interpretability of rules, however, depending on the nature of the underlying data the number of induced rules can be intractable large. Even reasonably sized result sets may contain a large amount of rules that are uninteresting to the user because they are too general, are already known or do not match other user-related intuitive criteria. We allow the user to model his conception of interestingness by means of linguistic expressions on rule evaluation measures and compound propositions of higher order (i.e., temporal changes of rule properties). Multiple such linguistic concepts can be considered a set of fuzzy patterns (Fuzzy Sets and Systems 28(3):313-331, 1988) and allow for the partition of the initial rule set into fuzzy fragments that contain rules of similar membership to a user’s concept (Höppner et al., Fuzzy Clustering, Wiley, Chichester, 1999; Computational Statistics and Data Analysis 51(1):192-214, 2006; Advances in Fuzzy Clustering and Its Applications, chap. 1, pp. 3-30, Wiley, New York, 2007). With appropriate visualization methods that extent previous rule set visualizations (Foundations of Fuzzy Logic and Soft Computing, Lecture Notes in Computer Science, vol. 4529, pp. 295-303, Springer, Berlin, 2007) we allow the user to instantly assess the matching of his concepts against the rule set.

  19. Fuzzy Logic Connectivity in Semiconductor Defect Clustering

    Energy Technology Data Exchange (ETDEWEB)

    Gleason, S.S.; Kamowski, T.P.; Tobin, K.W.

    1999-01-24

    In joining defects on semiconductor wafer maps into clusters, it is common for defects caused by different sources to overlap. Simple morphological image processing tends to either join too many unrelated defects together or not enough together. Expert semiconductor fabrication engineers have demonstrated that they can easily group clusters of defects from a common manufacturing problem source into a single signature. Capturing this thought process is ideally suited for fuzzy logic. A system of rules was developed to join disconnected clusters based on properties such as elongation, orientation, and distance. The clusters are evaluated on a pair-wise basis using the fuzzy rules and are joined or not joined based on a defuzzification and threshold. The system continuously re-evaluates the clusters under consideration as their fuzzy memberships change with each joining action. The fuzzy membership functions for each pair-wise feature, the techniques used to measure the features, and methods for improving the speed of the system are all developed. Examples of the process are shown using real-world semiconductor wafer maps obtained from chip manufacturers. The algorithm is utilized in the Spatial Signature Analyzer (SSA) software, a joint development project between Oak Ridge National Lab (ORNL) and SEMATECH.

  20. Fuzzy Logic Connectivity in Semiconductor Defect Clustering

    Energy Technology Data Exchange (ETDEWEB)

    Gleason, S.S.; Kamowski, T.P.; Tobin, K.W.

    1999-01-24

    In joining defects on semiconductor wafer maps into clusters, it is common for defects caused by different sources to overlap. Simple morphological image processing tends to either join too many unrelated defects together or not enough together. Expert semiconductor fabrication engineers have demonstrated that they can easily group clusters of defects from a common manufacturing problem source into a single signature. Capturing this thought process is ideally suited for fuzzy logic. A system of rules was developed to join disconnected clusters based on properties such as elongation, orientation, and distance. The clusters are evaluated on a pair-wise basis using the fuzzy rules and are joined or not joined based on a defuzzification and threshold. The system continuously re-evaluates the clusters under consideration as their fuzzy memberships change with each joining action. The fuzzy membership functions for each pair-wise feature, the techniques used to measure the features, and methods for improving the speed of the system are all developed. Examples of the process are shown using real-world semiconductor wafer maps obtained from chip manufacturers. The algorithm is utilized in the Spatial Signature Analyzer (SSA) software, a joint development project between Oak Ridge National Lab (ORNL) and SEMATECH.

  1. FAULT DIAGNOSIS BASED ON INTE- GRATION OF CLUSTER ANALYSIS,ROUGH SET METHOD AND FUZZY NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    Feng Zhipeng; Song Xigeng; Chu Fulei

    2004-01-01

    In order to increase the efficiency and decrease the cost of machinery diagnosis, a hybrid system of computational intelligence methods is presented. Firstly, the continuous attributes in diagnosis decision system are discretized with the self-organizing map (SOM) neural network. Then, dynamic reducts are computed based on rough set method, and the key conditions for diagnosis are found according to the maximum cluster ratio. Lastly, according to the optimal reduct, the adaptive neuro-fuzzy inference system (ANFIS) is designed for fault identification. The diagnosis of a diesel verifies the feasibility of engineering applications.

  2. Performance comparison of fuzzy and non-fuzzy classification methods

    Directory of Open Access Journals (Sweden)

    B. Simhachalam

    2016-07-01

    Full Text Available In data clustering, partition based clustering algorithms are widely used clustering algorithms. Among various partition algorithms, fuzzy algorithms, Fuzzy c-Means (FCM, Gustafson–Kessel (GK and non-fuzzy algorithm, k-means (KM are most popular methods. k-means and Fuzzy c-Means use standard Euclidian distance measure and Gustafson–Kessel uses fuzzy covariance matrix in their distance metrics. In this work, a comparative study of these algorithms with different famous real world data sets, liver disorder and wine from the UCI repository is presented. The performance of the three algorithms is analyzed based on the clustering output criteria. The results were compared with the results obtained from the repository. The results showed that Gustafson–Kessel produces close results to Fuzzy c-Means. Further, the experimental results demonstrate that k-means outperforms the Fuzzy c-Means and Gustafson–Kessel algorithms. Thus the efficiency of k-means is better than that of Fuzzy c-Means and Gustafson–Kessel algorithms.

  3. A hybrid method based on fuzzy clustering and local region-based level set for segmentation of inhomogeneous medical images.

    Science.gov (United States)

    Rastgarpour, Maryam; Shanbehzadeh, Jamshid; Soltanian-Zadeh, Hamid

    2014-08-01

    medical images are more affected by intensity inhomogeneity rather than noise and outliers. This has a great impact on the efficiency of region-based image segmentation methods, because they rely on homogeneity of intensities in the regions of interest. Meanwhile, initialization and configuration of controlling parameters affect the performance of level set segmentation. To address these problems, this paper proposes a new hybrid method that integrates a local region-based level set method with a variation of fuzzy clustering. Specifically it takes an information fusion approach based on a coarse-to-fine framework that seamlessly fuses local spatial information and gray level information with the information of the local region-based level set method. Also, the controlling parameters of level set are directly computed from fuzzy clustering result. This approach has valuable benefits such as automation, no need to prior knowledge about the region of interest (ROI), robustness on intensity inhomogeneity, automatic adjustment of controlling parameters, insensitivity to initialization, and satisfactory accuracy. So, the contribution of this paper is to provide these advantages together which have not been proposed yet for inhomogeneous medical images. Proposed method was tested on several medical images from different modalities for performance evaluation. Experimental results approve its effectiveness in segmenting medical images in comparison with similar methods.

  4. Fuzzy Clustering of Multiple Instance Data

    Science.gov (United States)

    2015-11-30

    NO. 0704-0188 3. DATES COVERED (From - To) - UU UU UU UU 10-03-2016 Approved for public release; distribution is unlimited. Fuzzy Clustering of...RETURN YOUR FORM TO THE ABOVE ADDRESS. University of Louisville 2301 S. Third Street Jouett Hall Louisville, KY 40208 -1838 ABSTRACT Fuzzy Clustering ...and identify K target concepts simultaneously. The proposed algorithm, called Fuzzy Clustering of Multiple Instance data (FCMI), is tested and

  5. Supplier Segmentation using Fuzzy Linguistic Preference Relations and Fuzzy Clustering

    Directory of Open Access Journals (Sweden)

    Pegah Sagheb Haghighi

    2014-04-01

    Full Text Available In an environment characterized by its competitiveness, managing and monitoring relationships with suppliers are of the essence. Supplier management includes supplier segmentation. Existing literature demonstrates that suppliers are mostly segmented by computing their aggregated scores, without taking each supplier’s criterion value into account. The principle aim of this paper is to propose a supplier segmentation method that compares each supplier’s criterion value with exactly the same criterion of other suppliers. The Fuzzy Linguistic Preference Relations (LinPreRa based Analytic Hierarchy Process (AHP is first used to find the weight of each criterion. Then, Fuzzy c-means algorithm is employed to cluster suppliers based on their membership degrees. The obtained results show that the proposed method enhances the quality of the previous findings.

  6. A physical analogy to fuzzy clustering

    DEFF Research Database (Denmark)

    Jantzen, Jan

    2004-01-01

    This tutorial paper provides an interpretation of the membership assignment in the fuzzy clustering algorithm fuzzy c-means. The membership of a data point to several clusters is shown to be analogous to the gravitational forces between bodies of mass. This provides an alternative way to explain...

  7. A physical analogy to fuzzy clustering

    DEFF Research Database (Denmark)

    Jantzen, Jan

    2004-01-01

    This tutorial paper provides an interpretation of the membership assignment in the fuzzy clustering algorithm fuzzy c-means. The membership of a data point to several clusters is shown to be analogous to the gravitational forces between bodies of mass. This provides an alternative way to explain ...

  8. Information Clustering Based on Fuzzy Multisets.

    Science.gov (United States)

    Miyamoto, Sadaaki

    2003-01-01

    Proposes a fuzzy multiset model for information clustering with application to information retrieval on the World Wide Web. Highlights include search engines; term clustering; document clustering; algorithms for calculating cluster centers; theoretical properties concerning clustering algorithms; and examples to show how the algorithms work.…

  9. A fast and robust level set method for image segmentation using fuzzy clustering and lattice Boltzmann method.

    Science.gov (United States)

    Balla-Arabé, Souleymane; Gao, Xinbo; Wang, Bin

    2013-06-01

    In the last decades, due to the development of the parallel programming, the lattice Boltzmann method (LBM) has attracted much attention as a fast alternative approach for solving partial differential equations. In this paper, we first designed an energy functional based on the fuzzy c-means objective function which incorporates the bias field that accounts for the intensity inhomogeneity of the real-world image. Using the gradient descent method, we obtained the corresponding level set equation from which we deduce a fuzzy external force for the LBM solver based on the model by Zhao. The method is fast, robust against noise, independent to the position of the initial contour, effective in the presence of intensity inhomogeneity, highly parallelizable and can detect objects with or without edges. Experiments on medical and real-world images demonstrate the performance of the proposed method in terms of speed and efficiency.

  10. Fuzzy sets, rough sets, multisets and clustering

    CERN Document Server

    Dahlbom, Anders; Narukawa, Yasuo

    2017-01-01

    This book is dedicated to Prof. Sadaaki Miyamoto and presents cutting-edge papers in some of the areas in which he contributed. Bringing together contributions by leading researchers in the field, it concretely addresses clustering, multisets, rough sets and fuzzy sets, as well as their applications in areas such as decision-making. The book is divided in four parts, the first of which focuses on clustering and classification. The second part puts the spotlight on multisets, bags, fuzzy bags and other fuzzy extensions, while the third deals with rough sets. Rounding out the coverage, the last part explores fuzzy sets and decision-making.

  11. Statistical Methods for Fuzzy Data

    CERN Document Server

    Viertl, Reinhard

    2011-01-01

    Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data. Also the results of measurements can be best described by using fuzzy numbers and fuzzy vectors respectively. Statistical analysis methods have to be adapted for the analysis of fuzzy data. In this book, the foundations of the description of fuzzy data are explained, including methods on how to obtain the characterizing function of fuzzy m

  12. Hesitant fuzzy agglomerative hierarchical clustering algorithms

    Science.gov (United States)

    Zhang, Xiaolu; Xu, Zeshui

    2015-02-01

    Recently, hesitant fuzzy sets (HFSs) have been studied by many researchers as a powerful tool to describe and deal with uncertain data, but relatively, very few studies focus on the clustering analysis of HFSs. In this paper, we propose a novel hesitant fuzzy agglomerative hierarchical clustering algorithm for HFSs. The algorithm considers each of the given HFSs as a unique cluster in the first stage, and then compares each pair of the HFSs by utilising the weighted Hamming distance or the weighted Euclidean distance. The two clusters with smaller distance are jointed. The procedure is then repeated time and again until the desirable number of clusters is achieved. Moreover, we extend the algorithm to cluster the interval-valued hesitant fuzzy sets, and finally illustrate the effectiveness of our clustering algorithms by experimental results.

  13. Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis.

    Science.gov (United States)

    Kim, Sunmee; Choi, Ji Yeh; Hwang, Heungsun

    2017-01-01

    Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.

  14. Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network.

    Science.gov (United States)

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.

  15. Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

    Science.gov (United States)

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

  16. EFFICIENT SUBSPACE CLUSTERING FOR HIGHER DIMENSIONAL DATA USING FUZZY ENTROPY

    Institute of Scientific and Technical Information of China (English)

    C.PALANISAMY; S.SELVAN

    2009-01-01

    In this paper we propose a novel method for identifying relevant subspaces using fuzzy entropy and perform clustering. This measure discriminates the real distribution better by using membership functions for measuring class match degrees. Hence the fuzzy entropy reflects more information in the actual disbution of patterns in the subspaces. We use a heuristic procedure based on the silhouette criterion to find the number of clusters. The presented theories and algorithms are evaluated through experiments on a collection of benchmark data sets. Empirical results have shown its favorable performance in comparison with several other clustering algorithms.

  17. Logistics Enterprise Evaluation Model Based On Fuzzy Clustering Analysis

    Science.gov (United States)

    Fu, Pei-hua; Yin, Hong-bo

    In this thesis, we introduced an evaluation model based on fuzzy cluster algorithm of logistics enterprises. First of all,we present the evaluation index system which contains basic information, management level, technical strength, transport capacity,informatization level, market competition and customer service. We decided the index weight according to the grades, and evaluated integrate ability of the logistics enterprises using fuzzy cluster analysis method. In this thesis, we introduced the system evaluation module and cluster analysis module in detail and described how we achieved these two modules. At last, we gave the result of the system.

  18. Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs).

    Science.gov (United States)

    Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

    2014-12-01

    In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature.

  19. Fuzzy Clustering Validity for Spatial Data%空间数据模糊聚类的有效性

    Institute of Scientific and Technical Information of China (English)

    胡春春; 孟令奎; 史文中

    2008-01-01

    The validity measurement of fuzzy clustering is a key problem. If clustering is formed, it needs a kind of machine to verify its validity. To make mining more accountable, comprehensible and with a usable spatial pattern, it is necessary to first detect whether the data set has a clustered structure or not before clustering. This paper discusses a detection method for clustered patterns and a fuzzy clustering algorithm, and studies the validity function of the result produced by fuzzy clustering based on two aspects, which reflect the uncertainty of classification during fuzzy partition and spatial location features of spatial data, and proposes a new validity function of fuzzy clustering for spatial data. The experimental result indicates that the new validity function can accurately measure the validity of the results of fuzzy clustering. Especially, for the result of fuzzy clustering of spatial data, it is robust and its classification result is better when compared to other indices.

  20. Comparing Fuzzy Possibilistic Methods on Critical Objects

    DEFF Research Database (Denmark)

    Yazdani, Hossein; Ortiz-Arroyo, Daniel; Choros, Kazimierz;

    2016-01-01

    Providing a flexible environment to process data objects is a desirable goal of machine learning algorithms. In fuzzy and possibilistic methods, the relevance of data objects is evaluated and a membership degree is assigned. However, some critical objects objects have the potential ability to affect...... the performance of the clustering algorithms if they remain in a specific cluster or they are moved into another. In this paper we analyze and compare how critical objects affect the behaviour of fuzzy possibilistic methods in several data sets. The comparison is based on the accuracy and ability of learning...

  1. A new fusion algorithm for fuzzy clustering

    Directory of Open Access Journals (Sweden)

    Ivan Vidović

    2014-12-01

    Full Text Available In this paper, we have considered the merging problem of two ellipsoidal clusters in order to construct a new fusion algorithm for fuzzy clustering. We have proposed a criterion for merging two ellipsoidal clusters ∏1, ∏2 with associated main Mahalanobis circles Ej(cj,σj, where cj is the centroid and σ^2j is the Mahalanobis variance of cluster ∏j . Based on the well-known Davies-Bouldin index, we have constructed a new fusion algorithm. The criterion has been tested on several data sets, and the performance of the fusion algorithm has been demonstrated on an illustrative example.

  2. Evaluation of Fuzzy Pareto Solution Set by Using Fuzzy Relation Based Clustering Approach For Fuzzy Multi-Response Experiments

    Directory of Open Access Journals (Sweden)

    Özlem Türkşen

    2013-01-01

    Full Text Available The solution set of a multi-response experiment is characterized by Pareto solution set. In this paper, the multi-response experiment is dealed in a fuzzy framework. The responses and model parameters are considered as triangular fuzzy numbers which indicate the uncertainty of the data set. Fuzzy least square approach and fuzzy modified NSGA-II (FNSGA-II are used for modeling and optimization, respectively. The obtained fuzzy Pareto solution set is grouped by using fuzzy relational clustering approach. Therefore, it could be easier to choose the alternative solutions to make better decision. A fuzzy response valued real data set is used as an application.

  3. A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.

    Science.gov (United States)

    Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing

    2017-01-01

    An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.

  4. Fuzzy clustering of physicochemical and biochemical properties of amino acids.

    Science.gov (United States)

    Saha, Indrajit; Maulik, Ujjwal; Bandyopadhyay, Sanghamitra; Plewczynski, Dariusz

    2012-08-01

    In this article, we categorize presently available experimental and theoretical knowledge of various physicochemical and biochemical features of amino acids, as collected in the AAindex database of known 544 amino acid (AA) indices. Previously reported 402 indices were categorized into six groups using hierarchical clustering technique and 142 were left unclustered. However, due to the increasing diversity of the database these indices are overlapping, therefore crisp clustering method may not provide optimal results. Moreover, in various large-scale bioinformatics analyses of whole proteomes, the proper selection of amino acid indices representing their biological significance is crucial for efficient and error-prone encoding of the short functional sequence motifs. In most cases, researchers perform exhaustive manual selection of the most informative indices. These two facts motivated us to analyse the widely used AA indices. The main goal of this article is twofold. First, we present a novel method of partitioning the bioinformatics data using consensus fuzzy clustering, where the recently proposed fuzzy clustering techniques are exploited. Second, we prepare three high quality subsets of all available indices. Superiority of the consensus fuzzy clustering method is demonstrated quantitatively, visually and statistically by comparing it with the previously proposed hierarchical clustered results. The processed AAindex1 database, supplementary material and the software are available at http://sysbio.icm.edu.pl/aaindex/ .

  5. Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical Relationship

    Directory of Open Access Journals (Sweden)

    Wangren Qiu

    2015-01-01

    Full Text Available In view of techniques for constructing high-order fuzzy time series models, there are three types which are based on advanced algorithms, computational method, and grouping the fuzzy logical relationships. The last type of models is easy to be understood by the decision maker who does not know anything about fuzzy set theory or advanced algorithms. To deal with forecasting problems, this paper presented novel high-order fuzz time series models denoted as GTS (M, N based on generalized fuzzy logical relationships and automatic clustering. This paper issued the concept of generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the procedure of the proposed model was implemented on forecasting enrollment data at the University of Alabama. To show the considerable outperforming results, the proposed approach was also applied to forecasting the Shanghai Stock Exchange Composite Index. Finally, the effects of parameters M and N, the number of order, and concerned principal fuzzy logical relationships, on the forecasting results were also discussed.

  6. Fuzzy Modeled K-Cluster Quality Mining of Hidden Knowledge for Decision Support

    Directory of Open Access Journals (Sweden)

    S. Parkash  Kumar

    2011-01-01

    Full Text Available Problem statement: The work presented Fuzzy Modeled K-means Cluster Quality Mining of hidden knowledge for Decision Support. Based on the number of clusters, number of objects in each cluster and its cohesiveness, precision and recall values, the cluster quality metrics is measured. The fuzzy k-means is adapted approach by using heuristic method which iterates the cluster to form an efficient valid cluster. With the obtained data clusters, quality assessment is made by predictive mining using decision tree model. Validation criteria focus on the quality metrics of the institution features for cluster formation and handle efficiently the arbitrary shaped clusters. Approach: The proposed work presented a fuzzy k-means cluster algorithm in the formation of student, faculty and infrastructural clusters based on the performance, skill set and facilitation availability respectively. The knowledge hidden among the educational data set is extracted through Fuzzy k-means cluster an unsupervised learning depends on certain initiation values to define the subgroups present in the data set. Results: Based on the features of the dataset and input parameters cluster formation vary, which motivates the clarification of cluster validity. The results of quality indexed fuzzy k-means shows better cluster validation compared to that of traditional k-family algorithm. Conclusion: The experimental results of cluster validation scheme confirm the reliability of validity index showing that it performs better than other k-family clusters.

  7. Water quality assessment for Ulansuhai Lake using fuzzy clustering and pattern recognition

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Water quality assessment of lakes is important to determine functional zones of water use. Considering the fuzziness during the partitioning process for lake water quality in an arid area, a multiplex model of fuzzy clustering with pattern recognition was developed by integrating transitive closure method, ISODATA algorithm in fuzzy clustering and fuzzy pattern recognition. The model was applied to partition the Ulansuhai Lake, a typical shallow lake in arid climate zone in the west part of Inner Mongolia, China and grade the condition of water quality divisions. The results showed that the partition well matched the real conditions of the lake, and the method has been proved accurate in the application.

  8. A fuzzy-clustering analysis based phonetic tied-mixture HMM

    Institute of Scientific and Technical Information of China (English)

    XU Xianghua; ZHU Jie; GUO Qiang

    2005-01-01

    To efficiently decrease the size of parameters and improve the robustness of parameters training, a fuzzy clustering based phonetic tied-mixture model, FPTM, is presented.The Gaussian codebook of FPTM is synthesized from Gaussian components belonging to the same root node in phonetic decision tree. Fuzzy clustering method is further used for FPTM covariance sharing. Experimental results show that compared with the conventional PTM with approximately the same parameters size, FPTM decrease the size of Gaussian weights by 77.59% and increases word accuracy by 7.92%, which proves Gaussian fuzzy clustering is efficient. Compared with FPTM, covariance-shared FPTM decreases word error rate by 1.14% , which proves the combined fuzzy clustering for both Gaussian and covariance is superior to Gaussian fuzzy clustering alone.

  9. Fuzzy Set Methods for Object Recognition in Space Applications

    Science.gov (United States)

    Keller, James M. (Editor)

    1992-01-01

    Progress on the following four tasks is described: (1) fuzzy set based decision methodologies; (2) membership calculation; (3) clustering methods (including derivation of pose estimation parameters), and (4) acquisition of images and testing of algorithms.

  10. Application of Fuzzy Cluster Analysis Method in Evaluating Relevant Index and Recognizing Risks of Coronary Heart Disease in the Aged

    Institute of Scientific and Technical Information of China (English)

    耿辉; 杨玉坤

    2003-01-01

    The risk recognition model for preventing and monitoring the Coronary Heart Diseases (CHD) in the aged is proposed, which is based on the testing results of four indexes and includes Low Density Lipoprotein (LDL), Total Cholesterol (TC), Triglyceridemia (TG)and age. Some people who took the health checkup in Shanghai Xinhua Hospital are classified into 3 groups,and each group is associated with prevalence risk of contracting CHD. Then the fuzzy recognition method is applied to evaluate the risk of CHD. The accuracy rate is up to 85%. The model is applicable to not only analysis of risk in medical but also analysis of risk in finance, insurance and some other fields.

  11. A Color Texture Image Segmentation Method Based on Fuzzy c-Means Clustering and Region-Level Markov Random Field Model

    Directory of Open Access Journals (Sweden)

    Guoying Liu

    2015-01-01

    Full Text Available This paper presents a variation of the fuzzy local information c-means clustering (FLICM algorithm that provides color texture image clustering. The proposed algorithm incorporates region-level spatial, spectral, and structural information in a novel fuzzy way. The new algorithm, called RFLICM, combines FLICM and region-level Markov random field model (RMRF together to make use of large scale interactions between image patches instead of pixels. RFLICM can overcome the weakness of FLICM when dealing with textured images and at the same time enhances the clustering performance. The major characteristic of RFLICM is the use of a region-level fuzzy factor, aiming to guarantee texture homogeneity and preserve region boundaries. Experiments performed on synthetic and remote sensing images show that RFLICM is effective in providing accuracy to color texture images.

  12. Fuzzy Rules for Ant Based Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    Amira Hamdi

    2016-01-01

    Full Text Available This paper provides a new intelligent technique for semisupervised data clustering problem that combines the Ant System (AS algorithm with the fuzzy c-means (FCM clustering algorithm. Our proposed approach, called F-ASClass algorithm, is a distributed algorithm inspired by foraging behavior observed in ant colonyT. The ability of ants to find the shortest path forms the basis of our proposed approach. In the first step, several colonies of cooperating entities, called artificial ants, are used to find shortest paths in a complete graph that we called graph-data. The number of colonies used in F-ASClass is equal to the number of clusters in dataset. Hence, the partition matrix of dataset founded by artificial ants is given in the second step, to the fuzzy c-means technique in order to assign unclassified objects generated in the first step. The proposed approach is tested on artificial and real datasets, and its performance is compared with those of K-means, K-medoid, and FCM algorithms. Experimental section shows that F-ASClass performs better according to the error rate classification, accuracy, and separation index.

  13. Fuzzy Temporal Clustering Approach for E-Commerce Websites

    Directory of Open Access Journals (Sweden)

    Sudhamathy G.

    2012-07-01

    Full Text Available In this paper a novel approach for clustering of web logs data and to predict intelligent recommendations on the E-Commerce web sites is proposed so as to improve the marketing strategy and to improve customer loyalty. Fuzzy Temporal Clustering Approach (FTCA performs clustering of the web site visitors and the web site pages based on the frequency of visit and time spent. Time plays a crucial role in the analysis of web usage. Hence these clusters are studied over a period of time to study the migration behaviour of the users and the pages across periods. Such a study can provide intelligentrecommendations for the E-Commerce web sites that focus on specific product recommendations and behavioural targeting. Experimental evaluation of the method has proved that this approach FTCA is most efficient, easy to use and a useful clustering approach.

  14. The application of genetic fuzzy clustering in bad data identification

    Science.gov (United States)

    Liu, Yunjing; Gu, Deying

    2006-11-01

    Power system static state estimation is aimed at providing modern electric control centers with accurate and reliable real-time databases. To this end, not only should the state estimator be able to filter out random observation noise but it should also be able to detect the existence, identify the locations and remove the effects of bad data. Detecting and identifying bad data is very important in state estimation of power system. A new method presented in this paper is fuzzy clustering with genetic search. And simulation data proves that error contamination and submergence can be reduced so that real bad data can be detected and identified. A key advantage of the proposed method is that the clustering is independent of the space distribution of input samples. This method possesses characteristics so faster convergence rate and more exact clustering results than some typical clustering algorithms.

  15. An Efficient Fuzzy Clustering-Based Approach for Intrusion Detection

    CERN Document Server

    Nguyen, Huu Hoa; Darmont, Jérôme

    2011-01-01

    The need to increase accuracy in detecting sophisticated cyber attacks poses a great challenge not only to the research community but also to corporations. So far, many approaches have been proposed to cope with this threat. Among them, data mining has brought on remarkable contributions to the intrusion detection problem. However, the generalization ability of data mining-based methods remains limited, and hence detecting sophisticated attacks remains a tough task. In this thread, we present a novel method based on both clustering and classification for developing an efficient intrusion detection system (IDS). The key idea is to take useful information exploited from fuzzy clustering into account for the process of building an IDS. To this aim, we first present cornerstones to construct additional cluster features for a training set. Then, we come up with an algorithm to generate an IDS based on such cluster features and the original input features. Finally, we experimentally prove that our method outperform...

  16. Mining fuzzy conceptual clusters and constructing the fuzzy conceptual frame lattices

    Science.gov (United States)

    Narang, Vibhu; Kumar, Naveen

    2004-04-01

    The key idea here is to use formal concept analysis and fuzzy membership criterion to partition the data space into clusters and provide knowledge through fuzzy lattices. The procedures, written here, are regarded as mapping or transform of the original space (samples) onto concepts. The mapping is further given the fuzzy membership criteria for clustering from which the clustered concepts of various degrees are found. Bucket hashing measure has been used as a measure of similarity in the proposed algorithm. The concepts are evaluated on the basis of this criterion and then they are clustered. The intuitive appeal of this approach lies in the fact that once the concepts are clustered, the data analyst is equipped with the concept measure as well as the identification of the bridging points. An interactive concept map visualization technique called Fuzzy Conceptual Frame Lattice or Fuzzy Concept Lattices is presented for user-guided knowledge discovery from the knowledge base.

  17. FUZZY IDENTIFICATION METHOD BASED ON A NEW OBJECTIVE FUNCTION

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A method of fuzzy identification based on a new objective function is proposed. The method could deal with the issue that input variables of a system have an effect on the input space while output variables of the system do not exert an influence on the input space in the proposed objective functions of fuzzy clustering. The method could simultaneously solve the problems about structure identification and parameter estimation; thus it makes the fuzzy model become optimal. Simulation example demonstrates that the method could identify non-linear systems and obviously improve modeling accuracy.

  18. An infared polarization image fusion method based on NSCT and fuzzy C-means clustering segmentation algorithms

    Science.gov (United States)

    Yu, Xuelian; Chen, Qian; Gu, Guohua; Qian, Weixian; Xu, Mengxi

    2014-11-01

    The integration between polarization and intensity images possessing complementary and discriminative information has emerged as a new and important research area. On the basis of the consideration that the resulting image has different clarity and layering requirement for the target and background, we propose a novel fusion method based on non-subsampled Contourlet transform (NSCT) and fuzzy C-means (FCM) segmentation for IR polarization and light intensity images. First, the polarization characteristic image is derived from fusion of the degree of polarization (DOP) and the angle of polarization (AOP) images using local standard variation and abrupt change degree (ACD) combined criteria. Then, the polarization characteristic image is segmented with FCM algorithm. Meanwhile, the two source images are respectively decomposed by NSCT. The regional energy-weighted and similarity measure are adopted to combine the low-frequency sub-band coefficients of the object. The high-frequency sub-band coefficients of the object boundaries are integrated through the maximum selection rule. In addition, the high-frequency sub-band coefficients of internal objects are integrated by utilizing local variation, matching measure and region feature weighting. The weighted average and maximum rules are employed independently in fusing the low-frequency and high-frequency components of the background. Finally, an inverse NSCT operation is accomplished and the final fused image is obtained. The experimental results illustrate that the proposed IR polarization image fusion algorithm can yield an improved performance in terms of the contrast between artificial target and cluttered background and a more detailed representation of the depicted scene.

  19. Fuzzy Document Clustering Approach using WordNet Lexical Categories

    Science.gov (United States)

    Gharib, Tarek F.; Fouad, Mohammed M.; Aref, Mostafa M.

    Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. This area is growing rapidly mainly because of the strong need for analysing the huge and large amount of textual data that reside on internal file systems and the Web. Text document clustering provides an effective navigation mechanism to organize this large amount of data by grouping their documents into a small number of meaningful classes. In this paper we proposed a fuzzy text document clustering approach using WordNet lexical categories and Fuzzy c-Means algorithm. Some experiments are performed to compare efficiency of the proposed approach with the recently reported approaches. Experimental results show that Fuzzy clustering leads to great performance results. Fuzzy c-means algorithm overcomes other classical clustering algorithms like k-means and bisecting k-means in both clustering quality and running time efficiency.

  20. Student academic performance analysis using fuzzy C-means clustering

    Science.gov (United States)

    Rosadi, R.; Akamal; Sudrajat, R.; Kharismawan, B.; Hambali, Y. A.

    2017-01-01

    Grade Point Average (GPA) is commonly used as an indicator of academic performance. Academic performance evaluations is a basic way to evaluate the progression of student performance, when evaluating student’s academic performance, there are occasion where the student data is grouped especially when the amounts of data is large. Thus, the pattern of data relationship within and among groups can be revealed. Grouping data can be done by using clustering method, where one of the methods is the Fuzzy C-Means algorithm. Furthermore, this algorithm is then applied to a set of student data form the Faculty of Mathematics and Natural Sciences, Padjadjaran University.

  1. Fuzzy support vector machines based on linear clustering

    Science.gov (United States)

    Xiong, Shengwu; Liu, Hongbing; Niu, Xiaoxiao

    2005-10-01

    A new Fuzzy Support Vector Machines (FSVMs) based on linear clustering is proposed in this paper. Its concept comes from the idea of linear clustering, selecting the data points near to the preformed hyperplane, which is formed on the training set including one positive and one negative training samples respectively. The more important samples near to the preformed hyperplane are selected by linear clustering technique, and the new FSVMs are formed on the more important data set. It integrates the merit of two kinds of FSVMs. The membership functions are defined using the relative distance between the data points and the preformed hyperplane during the training process. The fuzzy membership decision functions of multi-class FSVMs adopt the minimal value of all the decision functions of two-class FSVMs. To demonstrate the superiority of our methods, the benchmark data sets of machines learning databases are selected to verify the proposed FSVMs. The experimental results indicate that the proposed FSVMs can reduce the training data and running time, and its recognition rate is greater than or equal to that of FSVMs through selecting a suitable linear clustering parameter.

  2. An Investigation into Fuzzy Clustering and Classification.

    Science.gov (United States)

    1984-07-01

    Introduction to Fuzzy Sets The theory of fuzzy sets was developed by Lofti Zadeh in 1965(4). The impetus behind the introduction of the fuzzy set was...Syntactic Pattern Recoonition: An Introduction, Reading, Massachussetts, Addison-Wesley, 1978 4. Zadeh , Lofti A., "Fuzzy Sets", Information and...where the models based on crisp set theory fall short of providing a useful description of things, people, or places. So, as Professor Zadeh proposed

  3. Reverse triple I method of fuzzy reasoning

    Institute of Scientific and Technical Information of China (English)

    宋士吉; 吴澄

    2002-01-01

    A theory of reverse triple I method with sustention degree is presented by using the implication operator R0 in every step of the fuzzy reasoning. Its computation formulas of supremum for fuzzy modus ponens and infimum for fuzzy modus tollens are given respectively. Moreover, through the generalization of this problem, the corresponding formulas of ?-reverse triple I method with sustention degree are also obtained. In addition, the theory of reverse triple I method with restriction degree is proposed as well by using the operator R0, and the computation formulas of infimum for fuzzy modus ponens and supremum for fuzzy modus tollens are shown.

  4. Fuzzy Reasoning Methods by Choosing Different Fuzzy Counters and Analysis of Effect

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Different fuzzy reasoning methods were gave by choosing different fuzzy counters. This article generally introduced the basic structure of fuzzy controller,and compared and analysised the reasoning effect of fuzzy reasoning methods and the effect of computer simulating control basicly on different fuzzy counters.

  5. UNDERSTANDING OF FUZZY OPTIMIZATION:THEORIES AND METHODS

    Institute of Scientific and Technical Information of China (English)

    TANG Jiafu; WANG Dingwei; Richard Y K FUNG; Kai-Leung Yung

    2004-01-01

    A brief summary on and comprehensive understanding of fuzzy optimizationis presentedThis summary is made on aspects of fuzzy modelling and fuzzy optimization,classification and formulation for the fuzzy optimization problems, models and methods.The importance of interpretation of the problem and formulation of the optimal solutionin fuzzy sense are emphasized in the summary of the fuzzy optimization.

  6. Fuzzy Activation and Clustering of Nodes in a Hybrid Fibre Network Roll-out

    NARCIS (Netherlands)

    Kraak, J.J.; Phillipson, F.

    2015-01-01

    To design a Hybrid Fibre network, a selection of nodes is provided with active equipment and connected with fibre. If there is a need for a ring structure for high reliability, the activated nodes need to be clustered. In this paper a fuzzy method is proposed for this activation and clustering probl

  7. An Improved Fuzzy c-Means Clustering Algorithm Based on Shadowed Sets and PSO

    Directory of Open Access Journals (Sweden)

    Jian Zhang

    2014-01-01

    Full Text Available To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM based on particle swarm optimization (PSO and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect.

  8. Consistent linguistic fuzzy preference relations method with ranking fuzzy numbers

    Science.gov (United States)

    Ridzuan, Siti Amnah Mohd; Mohamad, Daud; Kamis, Nor Hanimah

    2014-12-01

    Multi-Criteria Decision Making (MCDM) methods have been developed to help decision makers in selecting the best criteria or alternatives from the options given. One of the well known methods in MCDM is the Consistent Fuzzy Preference Relation (CFPR) method, essentially utilizes a pairwise comparison approach. This method was later improved to cater subjectivity in the data by using fuzzy set, known as the Consistent Linguistic Fuzzy Preference Relations (CLFPR). The CLFPR method uses the additive transitivity property in the evaluation of pairwise comparison matrices. However, the calculation involved is lengthy and cumbersome. To overcome this problem, a method of defuzzification was introduced by researchers. Nevertheless, the defuzzification process has a major setback where some information may lose due to the simplification process. In this paper, we propose a method of CLFPR that preserves the fuzzy numbers form throughout the process. In obtaining the desired ordering result, a method of ranking fuzzy numbers is utilized in the procedure. This improved procedure for CLFPR is implemented to a case study to verify its effectiveness. This method is useful for solving decision making problems and can be applied to many areas of applications.

  9. A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

    Science.gov (United States)

    Wang, Zhihao; Yi, Jing

    2016-01-01

    For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291

  10. Eros-based Fuzzy Cluster Method for Longitudual Data%基于Eros距离的纵向数据模糊聚类方法

    Institute of Scientific and Technical Information of China (English)

    李会民; 闫健卓; 方丽英; 王普

    2013-01-01

    Considering the characteristics of longitudinal data set,such as multi-variates,missing data,unequal series length,and irregular time interval,an algorithm based on Eros distance similarity measure for longitudinal data is proposed.Eros distance is used in Fuzzy-C-Means cluster processing.First,preprocessing is done for unbalance longitudinal data set,which includes filling the missing data,reducing the randaut attributes,etc.Second,FErosCM Cluster method is used for claasification automatically,and takes into account information entropy for assessing the performance of cluster algorithm.Experiments show that this method is effective and efficient for longitudinal data classification.%针对纵向数据集的数据特征,如多维、含缺失值、序列不等间隔和不全等长等特点,研究一种基于Eros距离的纵向数据的相似性度量方法,并对模糊C均值聚类算法进行改进,提出一种基于Eros距离度量的模糊聚类数据处理方法.对于纵向数据集,首先进行缺失值填充、变量标准化等预处理,使用粗糙集理论对冗余属性进行约简,然后基于FErosCM聚类方法进行数据自动分类.对比实验证实此方法可用于纵向数据集的自动聚类处理,并使用信息熵作为聚类效果的评价手段.实验结果表明:无论在聚类效率还是准确度上,FErosCM方法对于纵向数据的分类处理均是有效可行的.

  11. Cluster Analysis and Fuzzy Query in Ship Maintenance and Design

    Science.gov (United States)

    Che, Jianhua; He, Qinming; Zhao, Yinggang; Qian, Feng; Chen, Qi

    Cluster analysis and fuzzy query win wide-spread applications in modern intelligent information processing. In allusion to the features of ship maintenance data, a variant of hypergraph-based clustering algorithm, i.e., Correlation Coefficient-based Minimal Spanning Tree(CC-MST), is proposed to analyze the bulky data rooting in ship maintenance process, discovery the unknown rules and help ship maintainers make a decision on various device fault causes. At the same time, revising or renewing an existed design of ship or device maybe necessary to eliminate those device faults. For the sake of offering ship designers some valuable hints, a fuzzy query mechanism is designed to retrieve the useful information from large-scale complicated and reluctant ship technical and testing data. Finally, two experiments based on a real ship device fault statistical dataset validate the flexibility and efficiency of the CC-MST algorithm. A fuzzy query prototype demonstrates the usability of our fuzzy query mechanism.

  12. Equipment Selection by using Fuzzy TOPSIS Method

    Science.gov (United States)

    Yavuz, Mahmut

    2016-10-01

    In this study, Fuzzy TOPSIS method was performed for the selection of open pit truck and the optimal solution of the problem was investigated. Data from Turkish Coal Enterprises was used in the application of the method. This paper explains the Fuzzy TOPSIS approaches with group decision-making application in an open pit coal mine in Turkey. An algorithm of the multi-person multi-criteria decision making with fuzzy set approach was applied an equipment selection problem. It was found that Fuzzy TOPSIS with a group decision making is a method that may help decision-makers in solving different decision-making problems in mining.

  13. Numerical method for solving fuzzy wave equation

    Science.gov (United States)

    Kermani, M. Afshar

    2013-10-01

    In this study a numerical method for solving "fuzzy partial differential equation" (FPDE) is considered. We present difference method to solve the FPDEs such as fuzzy hyperbolic equation, then see if stability of this method exist, and conditions for stability are given.

  14. Fuzzy Design Method of Product Quality Robustness

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    In order to express information on the quality grade of product, designed, the target value of product quality design was described with a fuzzy number in this paper. The rule of robust design with a fuzzy target was analyzed with fuzzy probability theory,then the principle and modeling method of fuzzy robust design for a high quality product were put forward. With this new method used, the high-quality ratio of the product de-signed could be increased, and the ability to resist the influence of various disturbing fac-tors ang noise factors could be enhanced.

  15. Fuzzy evaluation method using fuzzy rule approach in multicriteria analysis

    Directory of Open Access Journals (Sweden)

    Othman Mahmod

    2008-01-01

    Full Text Available A multicriteria analysis in ranking the quality of teaching using fuzzy rule is proposed. The proposed method uses the application of fuzzy sets and approximate reasoning in deciding the ranking of the quality of teaching in several courses. The proposed method introduces normalizing data which dampen the extreme value that exists in the data. The use of the model is suitable in evaluating situations that involve subjectivity, vagueness and imprecise information. Experimental results are comparable and the method performs better in some domains. .

  16. Risk Assessment for Bridges Safety Management during Operation Based on Fuzzy Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    Xia Hanyu

    2016-01-01

    Full Text Available In recent years, large span and large sea-crossing bridges are built, bridges accidents caused by improper operational management occur frequently. In order to explore the better methods for risk assessment of the bridges operation departments, the method based on fuzzy clustering algorithm is selected. Then, the implementation steps of fuzzy clustering algorithm are described, the risk evaluation system is built, and Taizhou Bridge is selected as an example, the quantitation of risk factors is described. After that, the clustering algorithm based on fuzzy equivalence is calculated on MATLAB 2010a. In the last, Taizhou Bridge operation management departments are classified and sorted according to the degree of risk, and the safety situation of operation departments is analyzed.

  17. Mapping Soil Texture of a Plain Area Using Fuzzy-c-Means Clustering Method Based on Land Surface Diurnal Temperature Difference

    Institute of Scientific and Technical Information of China (English)

    WANG De-Cai; ZHANG Gan-Lin; PAN Xian-Zhang; ZHAO Yu-Guo; ZHAO Ming-Song; WANG Gai-Fen

    2012-01-01

    The use of landscape covariates to estimate soil properties is not suitable for the areas of low relief due to the high variability of soil properties in similar topographic and vegetation conditions.A new method was implemented to map regional soil texture (in terms of sand,silt and clay contents) by hypothesizing that the change in the land surface diurnal temperature difference (DTD) is related to soil texture in case of a relatively homogeneous rainfall input.To examine this hypothesis,the DTDs from moderate resolution imagine spectroradiometer (MODIS) during a selected time period,i.e.,after a heavy rainfall between autumn harvest and autumn sowing,were classified using fuzzy-c-means (FCM) clustering.Six classes were generated,and for each class,the sand (> 0.05 mm),silt (0.002-0.05 mm) and clay (< 0.002 mm) contents at the location of maximum membership value were considered as the typical values of that class.A weighted average model was then used to digitally map soil texture.The results showed that the predicted map quite accurately reflected the regional soil variation.A validation dataset produced estimates of error for the predicted maps of sand,silt and clay contents at root mean of squared error values of 8.4%,7.8% and 2.3%,respectively,which is satisfactory in a practical context.This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in plain areas using easily available data sources.

  18. A new identification method for fuzzy linear models of non-linear dynamic systems

    NARCIS (Netherlands)

    de Bruin, H.A.E.; Roffel, B.

    1996-01-01

    The most promising methods for identifying a fuzzy model are data clustering, cluster merging and subsequent projection of the clusters on the input variable space. This article proposes to modify this procedure by adding a cluster rotation step, and a method for the direct calculation of the

  19. The implementation of hybrid clustering using fuzzy c-means and divisive algorithm for analyzing DNA human Papillomavirus cause of cervical cancer

    Science.gov (United States)

    Andryani, Diyah Septi; Bustamam, Alhadi; Lestari, Dian

    2017-03-01

    Clustering aims to classify the different patterns into groups called clusters. In this clustering method, we use n-mers frequency to calculate the distance matrix which is considered more accurate than using the DNA alignment. The clustering results could be used to discover biologically important sub-sections and groups of genes. Many clustering methods have been developed, while hard clustering methods considered less accurate than fuzzy clustering methods, especially if it is used for outliers data. Among fuzzy clustering methods, fuzzy c-means is one the best known for its accuracy and simplicity. Fuzzy c-means clustering uses membership function variable, which refers to how likely the data could be members into a cluster. Fuzzy c-means clustering works using the principle of minimizing the objective function. Parameters of membership function in fuzzy are used as a weighting factor which is also called the fuzzier. In this study we implement hybrid clustering using fuzzy c-means and divisive algorithm which could improve the accuracy of cluster membership compare to traditional partitional approach only. In this study fuzzy c-means is used in the first step to find partition results. Furthermore divisive algorithms will run on the second step to find sub-clusters and dendogram of phylogenetic tree. To find the best number of clusters is determined using the minimum value of Davies Bouldin Index (DBI) of the cluster results. In this research, the results show that the methods introduced in this paper is better than other partitioning methods. Finally, we found 3 clusters with DBI value of 1.126628 at first step of clustering. Moreover, DBI values after implementing the second step of clustering are always producing smaller IDB values compare to the results of using first step clustering only. This condition indicates that the hybrid approach in this study produce better performance of the cluster results, in term its DBI values.

  20. Fuzzy c-Means and Cluster Ensemble with Random Projection for Big Data Clustering

    Directory of Open Access Journals (Sweden)

    Mao Ye

    2016-01-01

    Full Text Available Because of its positive effects on dealing with the curse of dimensionality in big data, random projection for dimensionality reduction has become a popular method recently. In this paper, an academic analysis of influences of random projection on the variability of data set and the dependence of dimensions has been proposed. Together with the theoretical analysis, a new fuzzy c-means (FCM clustering algorithm with random projection has been presented. Empirical results verify that the new algorithm not only preserves the accuracy of original FCM clustering, but also is more efficient than original clustering and clustering with singular value decomposition. At the same time, a new cluster ensemble approach based on FCM clustering with random projection is also proposed. The new aggregation method can efficiently compute the spectral embedding of data with cluster centers based representation which scales linearly with data size. Experimental results reveal the efficiency, effectiveness, and robustness of our algorithm compared to the state-of-the-art methods.

  1. Record Matching Over Query Results Using Fuzzy Ontological Document Clustering

    Directory of Open Access Journals (Sweden)

    V.Vijayaraja

    2011-02-01

    Full Text Available Record matching is an essential step in duplicate detection as it identifies records representing same real-world entity. Supervised record matching methods require users to provide training data andtherefore cannot be applied for web databases where query results are generated on-the-fly. To overcome the problem, a new record matching method named Unsupervised Duplicate Elimination (UDE is proposed for identifying and eliminating duplicates among records in dynamic query results. The idea of this paper is to adjust the weights of record fields in calculating similarities among records. Two classifiers namely weight component similarity summing classifier, support vector machine classifier are iteratively employed with UDE where the first classifier utilizes the weights set to match records from different data sources. With the matched records as positive dataset and non duplicate records as negative set, the second classifier identifies new duplicates. Then, a new methodology to automatically interpret and cluster knowledge documents using an ontology schema is presented. Moreover, a fuzzy logic control approach is used to match suitable document cluster(s for given patents based on their derived ontological semantic webs. Thus, this paper takes advantage of similarity among records from web databases and solves the online duplicate detection problem.

  2. Mining Representative Subset Based on Fuzzy Clustering

    Institute of Scientific and Technical Information of China (English)

    ZHOU Hongfang; FENG Boqin; L(U) Lintao

    2007-01-01

    Two new concepts-fuzzy mutuality and average fuzzy entropy are presented. Then based on these concepts, a new algorithm-RSMA (representative subset mining algorithm) is proposed, which can abstract representative subset from massive data.To accelerate the speed of producing representative subset, an improved algorithm-ARSMA(accelerated representative subset mining algorithm) is advanced, which adopt combining putting forward with backward strategies. In this way, the performance of the algorithm is improved. Finally we make experiments on real datasets and evaluate the representative subset. The experiment shows that ARSMA algorithm is more excellent than RandomPick algorithm either on effectiveness or efficiency.

  3. An Extended Membrane System with Active Membranes to Solve Automatic Fuzzy Clustering Problems.

    Science.gov (United States)

    Peng, Hong; Wang, Jun; Shi, Peng; Pérez-Jiménez, Mario J; Riscos-Núñez, Agustín

    2016-05-01

    This paper focuses on automatic fuzzy clustering problem and proposes a novel automatic fuzzy clustering method that employs an extended membrane system with active membranes that has been designed as its computing framework. The extended membrane system has a dynamic membrane structure; since membranes can evolve, it is particularly suitable for processing the automatic fuzzy clustering problem. A modification of a differential evolution (DE) mechanism was developed as evolution rules for objects according to membrane structure and object communication mechanisms. Under the control of both the object's evolution-communication mechanism and the membrane evolution mechanism, the extended membrane system can effectively determine the most appropriate number of clusters as well as the corresponding optimal cluster centers. The proposed method was evaluated over 13 benchmark problems and was compared with four state-of-the-art automatic clustering methods, two recently developed clustering methods and six classification techniques. The comparison results demonstrate the superiority of the proposed method in terms of effectiveness and robustness.

  4. Weighted fuzzy clustering for (fuzzy constraints in multivariate image analysis–alternating least square of hyperspectral images

    Directory of Open Access Journals (Sweden)

    Siewert Hugelier

    2016-12-01

    Full Text Available In order to investigate hyperspectral images, many techniques such as multivariate image analysis (MIA or multivariate curve resolution–alternating least squares (MCR–ALS can be applied. When focusing on the use of MCR–ALS, constraints are of the utmost importance for a correct resolution of the data into its individual contributions. In this article, a fuzzy clustering pattern recognition method (fuzzy C-means is applied on experimental data in order to improve the results obtained within the MCR–ALS analysis. The big advantage of a fuzzy clustering technique over a hard clustering technique, such as k-means, is that the algorithm determines the probability of a pixel to be assigned to a component, indicating that a pixel can be part of multiple clusters (or components. This is, of course, an important property for dealing with data in which a lot of overlap between the components in the spatial direction occurs. This article deals briefly with the implementation of the constraint into the MCR–ALS algorithm and then shows the application of the constraint on an oil-in-water emulsion obtained by Raman spectroscopy, in which the different components can be decomposed in a clearer way and the interface between the oil and water bubbles becomes more visible.

  5. Hybrid fuzzy cluster ensemble framework for tumor clustering from biomolecular data.

    Science.gov (United States)

    Yu, Zhiwen; Chen, Hantao; You, Jane; Han, Guoqiang; Li, Le

    2013-01-01

    Cancer class discovery using biomolecular data is one of the most important tasks for cancer diagnosis and treatment. Tumor clustering from gene expression data provides a new way to perform cancer class discovery. Most of the existing research works adopt single-clustering algorithms to perform tumor clustering is from biomolecular data that lack robustness, stability, and accuracy. To further improve the performance of tumor clustering from biomolecular data, we introduce the fuzzy theory into the cluster ensemble framework for tumor clustering from biomolecular data, and propose four kinds of hybrid fuzzy cluster ensemble frameworks (HFCEF), named as HFCEF-I, HFCEF-II, HFCEF-III, and HFCEF-IV, respectively, to identify samples that belong to different types of cancers. The difference between HFCEF-I and HFCEF-II is that they adopt different ensemble generator approaches to generate a set of fuzzy matrices in the ensemble. Specifically, HFCEF-I applies the affinity propagation algorithm (AP) to perform clustering on the sample dimension and generates a set of fuzzy matrices in the ensemble based on the fuzzy membership function and base samples selected by AP. HFCEF-II adopts AP to perform clustering on the attribute dimension, generates a set of subspaces, and obtains a set of fuzzy matrices in the ensemble by performing fuzzy c-means on subspaces. Compared with HFCEF-I and HFCEF-II, HFCEF-III and HFCEF-IV consider the characteristics of HFCEF-I and HFCEF-II. HFCEF-III combines HFCEF-I and HFCEF-II in a serial way, while HFCEF-IV integrates HFCEF-I and HFCEF-II in a concurrent way. HFCEFs adopt suitable consensus functions, such as the fuzzy c-means algorithm or the normalized cut algorithm (Ncut), to summarize generated fuzzy matrices, and obtain the final results. The experiments on real data sets from UCI machine learning repository and cancer gene expression profiles illustrate that 1) the proposed hybrid fuzzy cluster ensemble frameworks work well on real

  6. Advances in theory and applications of fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The summarization and evaluation of the advances in fuzzy clustering theory are made in the aspects including the criterion functions, algorithm implementations, validity measurements and applications. Several important directions for a further study and the application prospects are also pointed out.

  7. FUZZY ARITHMETIC AND SOLVING OF THE STATIC GOVERNING EQUATIONS OF FUZZY FINITE ELEMENT METHOD

    Institute of Scientific and Technical Information of China (English)

    郭书祥; 吕震宙; 冯立富

    2002-01-01

    The key component of finite element analysis of structures with fuzzy parameters,which is associated with handling of some fuzzy information and arithmetic relation of fuzzy variables, was the solving of the governing equations of fuzzy finite element method. Based on a given interval representation of fuzzy numbers, some arithmetic rules of fuzzy numbers and fuzzy variables were developed in terms of the properties of interval arithmetic.According to the rules and by the theory of interval finite element method, procedures for solving the static governing equations of fuzzy finite element method of structures were presented. By the proposed procedure, the possibility distributions of responses of fuzzy structures can be generated in terms of the membership functions of the input fuzzy numbers.It is shown by a numerical example that the computational burden of the presented procedures is low and easy to implement. The effectiveness and usefulness of the presented procedures are also illustrated.

  8. Fuzzy-TOPSIS Method with Multi-goal

    Institute of Scientific and Technical Information of China (English)

    PANG Jin-hui; ZHANG Qiang

    2009-01-01

    To develop the technique for order preference by similarity to an ideal solution,namely,TOPSIS method with multi-goal in fuzzy decision environment.Firstly,a new approach to constructing fuzzy decision matrix by Choquet integral was proposed in muhi-goal decision system.Secondly,the concepts of fuzzy positive-ideal solution and fuzzy negative-ideal solution related to the fuzzy decision matrix were given.Finally,the credibility measure was adopted to calculate the distances to fuzzy positive-ideal solution and fuzzy negative-ideal solution.The presented fuzzy-TOPSIS method embodies well both the predetermined preferences and the weights of goals.

  9. Fuzzy nodes recognition based on spectral clustering in complex networks

    Science.gov (United States)

    Ma, Yang; Cheng, Guangquan; Liu, Zhong; Xie, Fuli

    2017-01-01

    In complex networks, information regarding the nodes is usually incomplete because of the effects of interference, noise, and other factors. This results in parts of the network being blurred and some information having an unknown source. In this paper, a spectral clustering algorithm is used to identify fuzzy nodes and solve network reconstruction problems. By changing the fuzzy degree of placeholders, we achieve various degrees of credibility and accuracy for the restored network. Our approach is verified by experiments using open source datasets and simulated data.

  10. Expected Value Method for Fuzzy Multiple Attribute Decision Making

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    This paper presents a fuzzy multiple attribute decision-making (FMADM) method in which the attribute weights and decision matrix elements (attribute values) are fuzzy variables. Fuzzy arithmetic and the expected value operator of fuzzy variables are used to develop the expected value method to solve the FMADM problem. A numerical example is given to demonstrate the feasibility and effectiveness of the method.

  11. Cognitive analysis of multiple sclerosis utilizing fuzzy cluster means

    Directory of Open Access Journals (Sweden)

    Imianvan Anthony Agboizebeta

    2012-01-01

    Full Text Available Multiple sclerosis, often called MS, is a disease that affects the central nervous system (the brain and spinal cord. Myelin provides insulation for nerve cells improves the conduction of impulses along the nerves and is important for maintaining the health of the nerves. In multiple sclerosis, inflammation causes the myelin to disappear. Genetic factors, environmental issues and viral infection may also play a role in developing the disease. Ms is characterized by life threatening symptoms such as; loss of balance, hearing problem and depression. The application of Fuzzy Cluster Means (FCM or Fuzzy CMean analysis to the diagnosis of different forms of multiple sclerosis is the focal point of this paper. Application of cluster analysis involves a sequence of methodological and analytical decision steps that enhances the quality and meaning of the clusters produced. Uncertainties associated with analysis of multiple sclerosis test data are eliminated by the system

  12. Cognitive analysis of multiple sclerosis utilizing fuzzy cluster means

    Directory of Open Access Journals (Sweden)

    Imianvan Anthony Agboizebeta

    2012-02-01

    Full Text Available Multiple sclerosis, often called MS, is a disease that affects the central nervous system (the brain andspinal cord. Myelin provides insulation for nerve cells improves the conduction of impulses along thenerves and is important for maintaining the health of the nerves. In multiple sclerosis, inflammationcauses the myelin to disappear. Genetic factors, environmental issues and viral infection may alsoplay a role in developing the disease. Ms is characterized by life threatening symptoms such as; loss ofbalance, hearing problem and depression. The application of Fuzzy Cluster Means (FCM or Fuzzy CMeananalysis to the diagnosis of different forms of multiple sclerosis is the focal point of this paper.Application of cluster analysis involves a sequence of methodological and analytical decision stepsthat enhances the quality and meaning of the clusters produced. Uncertainties associated withanalysis of multiple sclerosis test data are eliminated by the system

  13. A Geometric Fuzzy-Based Approach for Airport Clustering

    Directory of Open Access Journals (Sweden)

    Maria Nadia Postorino

    2014-01-01

    Full Text Available Airport classification is a common need in the air transport field due to several purposes—such as resource allocation, identification of crucial nodes, and real-time identification of substitute nodes—which also depend on the involved actors’ expectations. In this paper a fuzzy-based procedure has been proposed to cluster airports by using a fuzzy geometric point of view according to the concept of unit-hypercube. By representing each airport as a point in the given reference metric space, the geometric distance among airports—which corresponds to a measure of similarity—has in fact an intrinsic fuzzy nature due to the airport specific characteristics. The proposed procedure has been applied to a test case concerning the Italian airport network and the obtained results are in line with expectations.

  14. COMPARISON OF PURITY AND ENTROPY OF K-MEANS CLUSTERING AND FUZZY C MEANS CLUSTERING

    Directory of Open Access Journals (Sweden)

    Satya Chaitanya Sripada

    2011-06-01

    Full Text Available Clustering is one the main area in data mining literature. There are various algorithms for clustering. The evaluation of the performance isdone by validation measures. The external validation measures are used to measure the extent to which cluster labels affirm with theexternally given class labels. The aim of this paper is to compare the for K-means and Fuzzy C means clustering using the Purity andEntropy. The data used for evaluating the external measures is medical data.

  15. Classification of protein profiles using fuzzy clustering techniques

    DEFF Research Database (Denmark)

    Karemore, Gopal; Mullick, Jhinuk B.; Sujatha, R.

    2010-01-01

    -to-day   variation,   artifacts   due   to experimental   strategies,   inherent   uncertainty   in   pumping procedure which are very common activities during HPLC-LIF experiment.  Under  these  circumstances  we  demonstrate  how fuzzy clustering algorithm like Gath Geva followed by sammon mapping   outperform......   PCA   mapping   in   classifying   various cancers from healthy spectra with classification rate up to 95 % from  60%.  Methods  are  validated  using  various  clustering indexes   and   shows   promising   improvement   in   developing optical pathology like HPLC-LIF for early detection of various...

  16. Application of Fuzzy Matter Element Clustering Method in Evaluation of Sports Classroom Teaching Ability%模糊物元聚类法在体育教学评价中的应用

    Institute of Scientific and Technical Information of China (English)

    何杰明

    2012-01-01

    P.E teachers' teaching ability is the core link to realize physical education teaching quality.This paper uses the fuzzy matter-element theory,in order to P.E teachers as the research object,the sports classroom teaching ability evaluation index for feature,examination results mean values for fuzzy value,establish of teaching ability in classroom 4 dimensional fuzzy matter element matrix composite to be evaluated,through the fuzzy matter element correlation function transform,achieve matter-element to be evaluated distance,clustering,so as to achieve the purpose of the evaluation.For the effective implementation of physical education teachers teaching ability evaluation,promoting sports to improve the quality of teaching,to provide a simple and easy operation method.%体育教师课堂教学能力是实现体育教育教学质量核心环节。文章利用模糊物元理论,以体育教师个体为研究对象,以体育课堂教学能力评价指标为特征、考核成绩平均值为模糊量值,建立体育教师课堂教学能力4维复合待评模糊物元矩阵,通过模糊物元关联函数变换,求得待评物元距离,实现聚类,从而达到评价的目的。为有效实现体育教师课堂教学能力评价,促进体育教学质量提高,提供一种简单易行的操作方法。

  17. AN IMPROVED FUZZY CLUSTERING ALGORITHM FOR MICROARRAY IMAGE SPOTS SEGMENTATION

    Directory of Open Access Journals (Sweden)

    V.G. Biju

    2015-11-01

    Full Text Available An automatic cDNA microarray image processing using an improved fuzzy clustering algorithm is presented in this paper. The spot segmentation algorithm proposed uses the gridding technique developed by the authors earlier, for finding the co-ordinates of each spot in an image. Automatic cropping of spots from microarray image is done using these co-ordinates. The present paper proposes an improved fuzzy clustering algorithm Possibility fuzzy local information c means (PFLICM to segment the spot foreground (FG from background (BG. The PFLICM improves fuzzy local information c means (FLICM algorithm by incorporating typicality of a pixel along with gray level information and local spatial information. The performance of the algorithm is validated using a set of simulated cDNA microarray images added with different levels of AWGN noise. The strength of the algorithm is tested by computing the parameters such as the Segmentation matching factor (SMF, Probability of error (pe, Discrepancy distance (D and Normal mean square error (NMSE. SMF value obtained for PFLICM algorithm shows an improvement of 0.9 % and 0.7 % for high noise and low noise microarray images respectively compared to FLICM algorithm. The PFLICM algorithm is also applied on real microarray images and gene expression values are computed.

  18. DYNAMIC CHARACTERISTIC ANALYSIS OF FUZZY- STOCHASTIC TRUSS STRUCTURES BASED ON FUZZY FACTOR METHOD AND RANDOM FACTOR METHOD

    Institute of Scientific and Technical Information of China (English)

    MA Juan; CHEN Jian-jun; XU Ya-lan; JIANG Tao

    2006-01-01

    A new fuzzy stochastic finite element method based on the fuzzy factor method and random factor method is given and the analysis of structural dynamic characteristic for fuzzy stochastic truss structures is presented. Considering the fuzzy randomness of the structural physical parameters and geometric dimensions simultaneously, the structural stiffness and mass matrices are constructed based on the fuzzy factor method and random factor method; from the Rayleigh's quotient of structural vibration, the structural fuzzy random dynamic characteristic is obtained by means of the interval arithmetic;the fuzzy numeric characteristics of dynamic characteristic are then derived by using the random variable's moment function method and algebra synthesis method. Two examples are used to illustrate the validity and rationality of the method given. The advantage of this method is that the effect of the fuzzy randomness of one of the structural parameters on the fuzzy randomness of the dynamic characteristic can be reflected expediently and objectively.

  19. A New Method for Solving General Dual Fuzzy Linear Systems

    Directory of Open Access Journals (Sweden)

    M. Otadi

    2013-09-01

    Full Text Available . According to fuzzy arithmetic, general dual fuzzy linear system (GDFLS cannot be replaced by a fuzzy linear system (FLS. In this paper, we use new notation of fuzzy numbers and convert a GDFLS to two linear systems in crisp case, then we discuss complexity of the proposed method. Conditions for the existence of a unique fuzzy solution to n × n GDFLS are derived

  20. A New Method for Solving Fuzzy Linear Programs with Trapezoidal Fuzzy Numbers

    Directory of Open Access Journals (Sweden)

    Jagdeep Kaur

    2011-12-01

    Full Text Available Ganesan and Veeramani [Fuzzy linear programs with trapezoidal fuzzy numbers, Annals of Operations Research 143 (2006 305-315.] proposed a new method for solving a special type of fuzzy linear programming problems. In this paper a new method, named as Mehar's method, is proposed for solving the same type of fuzzy linear programming problems and it is shown that it is easy to apply the Mehar's method as compared to the existing method for solving the same type of fuzzy linear programming problems.

  1. Application of Fuzzy Clustering in Modeling of a Water Hydraulics System

    DEFF Research Database (Denmark)

    Zhou, Jianjun; Kroszynski, Uri

    2000-01-01

    This article presents a case study of applying fuzzy modeling techniques for a water hydraulics system. The obtained model is intended to provide a basis for model-based control of the system. Fuzzy clustering is used for classifying measured input-output data points into partitions. The fuzzy mo...

  2. A Substractive Clustering Based Fuzzy Hybrid Reference Control Design for Transient Response Improvement of PID Controller

    Directory of Open Access Journals (Sweden)

    Endra Joelianto

    2009-11-01

    Full Text Available The well known PID controller has inherent limitations in fulfilling simultaneously the conflicting control design objectives. Parameters of the tuned PID controller should trade off the requirement of tracking set-point performances, disturbance rejection and stability robustness. Combination of hybrid reference control (HRC with PID controller results in the transient response performances can be independently achieved without deteriorating the disturbance rejection properties and the stability robustness requirement. This paper proposes a fuzzy based HRC where the membership functions of the fuzzy logic system are obtained by using a substractive clustering technique. The proposed method guarantees the transient response performances satisfaction while preserving the stability robustness of the closed loop system controlled by the PID controller with effective and systematic procedures in designing the fuzzy hybrid reference control system.

  3. Systematic methods for the design of a class of fuzzy logic controllers

    Science.gov (United States)

    Yasin, Saad Yaser

    2002-09-01

    Fuzzy logic control, a relatively new branch of control, can be used effectively whenever conventional control techniques become inapplicable or impractical. Various attempts have been made to create a generalized fuzzy control system and to formulate an analytically based fuzzy control law. In this study, two methods, the left and right parameterization method and the normalized spline-base membership function method, were utilized for formulating analytical fuzzy control laws in important practical control applications. The first model was used to design an idle speed controller, while the second was used to control an inverted control problem. The results of both showed that a fuzzy logic control system based on the developed models could be used effectively to control highly nonlinear and complex systems. This study also investigated the application of fuzzy control in areas not fully utilizing fuzzy logic control. Three important practical applications pertaining to the automotive industries were studied. The first automotive-related application was the idle speed of spark ignition engines, using two fuzzy control methods: (1) left and right parameterization, and (2) fuzzy clustering techniques and experimental data. The simulation and experimental results showed that a conventional controller-like performance fuzzy controller could be designed based only on experimental data and intuitive knowledge of the system. In the second application, the automotive cruise control problem, a fuzzy control model was developed using parameters adaptive Proportional plus Integral plus Derivative (PID)-type fuzzy logic controller. Results were comparable to those using linearized conventional PID and linear quadratic regulator (LQR) controllers and, in certain cases and conditions, the developed controller outperformed the conventional PID and LQR controllers. The third application involved the air/fuel ratio control problem, using fuzzy clustering techniques, experimental

  4. Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance.

    Science.gov (United States)

    Park, Jong-Wook; Kwak, Hwan-Joo; Kang, Young-Chang; Kim, Dong W

    2016-01-01

    An advanced fuzzy potential field method for mobile robot obstacle avoidance is proposed. The potential field method primarily deals with the repulsive forces surrounding obstacles, while fuzzy control logic focuses on fuzzy rules that handle linguistic variables and describe the knowledge of experts. The design of a fuzzy controller--advanced fuzzy potential field method (AFPFM)--that models and enhances the conventional potential field method is proposed and discussed. This study also examines the rule-explosion problem of conventional fuzzy logic and assesses the performance of our proposed AFPFM through simulations carried out using a mobile robot.

  5. Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance

    Science.gov (United States)

    Park, Jong-Wook; Kwak, Hwan-Joo; Kang, Young-Chang; Kim, Dong W.

    2016-01-01

    An advanced fuzzy potential field method for mobile robot obstacle avoidance is proposed. The potential field method primarily deals with the repulsive forces surrounding obstacles, while fuzzy control logic focuses on fuzzy rules that handle linguistic variables and describe the knowledge of experts. The design of a fuzzy controller—advanced fuzzy potential field method (AFPFM)—that models and enhances the conventional potential field method is proposed and discussed. This study also examines the rule-explosion problem of conventional fuzzy logic and assesses the performance of our proposed AFPFM through simulations carried out using a mobile robot. PMID:27123001

  6. Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance

    Directory of Open Access Journals (Sweden)

    Jong-Wook Park

    2016-01-01

    Full Text Available An advanced fuzzy potential field method for mobile robot obstacle avoidance is proposed. The potential field method primarily deals with the repulsive forces surrounding obstacles, while fuzzy control logic focuses on fuzzy rules that handle linguistic variables and describe the knowledge of experts. The design of a fuzzy controller—advanced fuzzy potential field method (AFPFM—that models and enhances the conventional potential field method is proposed and discussed. This study also examines the rule-explosion problem of conventional fuzzy logic and assesses the performance of our proposed AFPFM through simulations carried out using a mobile robot.

  7. Risk analysis of dam based on artificial bee colony algorithm with fuzzy c-means clustering

    Energy Technology Data Exchange (ETDEWEB)

    Li, Haojin; Li, Junjie; Kang, Fei

    2011-05-15

    Risk analysis is a method which has been incorporated into infrastructure engineering. Fuzzy c-means clustering (FCM) is a simple and fast method utilized most of the time, but it can induce errors as it is sensitive to initialization. The aim of this paper was to propose a new method for risk analysis using an artificial bee colony algorithm (ABC) with FCM. This new technique is first explained and then applied on three experiments. Results demonstrated that the combination of artificial bee colony algorithm fuzzy c-means clustering (ABCFCM) is overcoming the FCM issue since it is not initialization sensitive and experiments showed that this algorithm is more accurate and than FCM. This paper provides a new tool for risk analysis which can be used for risk prioritizing and reinforcing dangerous dams in a more scientific way.

  8. AN IMPROVED ALGORITHM FOR SUPERVISED FUZZY C-MEANS CLUSTERING OF REMOTELY SENSED DATA

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    This paper describes an improved algorithm for fuzzy c-means clustering of remotely sensed data, by which the degree of fuzziness of the resultant classification is de creased as comparing with that by a conventional algorithm: that is , the classification accura cy is increased. This is achieved by incorporating covariance matrices at the level of individual classes rather than assuming a global one. Empirical results from a fuzzy classification of an Edinburgh suburban land cover confirmed the improved performance of the new algorithm for fuzzy c-means clustering, in particular when fuzziness is also accommodated in the assumed reference data.

  9. A method for solving fully fuzzy linear system with trapezoidal fuzzy numbers

    Directory of Open Access Journals (Sweden)

    A. Kumar

    2010-03-01

    Full Text Available Different methods have been proposed for finding the non-negative solution of fully fuzzy linear system (FFLS i.e. fuzzy linear system with fuzzy coefficients involving fuzzy variables. To the best of our knowledge, there is no method in the literature for finding the non-negative solution of a FFLS without any restriction on the coefficient matrix. In this paper a new computational method is proposed to solve FFLS without any restriction on the coefficient matrix by representing all the parameters as trapezoidal fuzzy numbers.

  10. Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

    Science.gov (United States)

    Zhang, Ying; Wang, Jun; Han, Dezhi; Wu, Huafeng; Zhou, Rundong

    2017-01-01

    Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes’ energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs’ election, we take nodes’ energies, nodes’ degree and neighbor nodes’ residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks. PMID:28671641

  11. Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks.

    Science.gov (United States)

    Zhang, Ying; Wang, Jun; Han, Dezhi; Wu, Huafeng; Zhou, Rundong

    2017-07-03

    Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes' energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs' election, we take nodes' energies, nodes' degree and neighbor nodes' residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.

  12. Application of Bibliographic Coupling versus Cited Titles Words in Patent Fuzzy Clustering

    Directory of Open Access Journals (Sweden)

    Anahita Kermani

    2013-03-01

    Full Text Available Attribute selection is one of the steps before patent clustering. Various attributes can be used for clustering. In this study, the effect of using citation and citation title words, respectively, in form of bibliographic coupling and citation title words sharing, were measured and compared with each other, as patent attributes. This study was done in an experimental method, on a collection of 717 US Patent cited in the patents belong to 977/774 subclass of US Patent Classification. Fuzzy C-means was used for patent clustering and extended BCubed precision and extended BCubed recall were used as evaluation measure. The results showed that the clustering produced by bibliographic coupling had better performance than clustering used citation title words and existence of cluster structure were in a wider range of exhaustivity than citation title words.

  13. Multivariate image segmentation with cluster size insensitive Fuzzy C-means

    NARCIS (Netherlands)

    Noordam, J.C.; Broek, van den W.H.A.M.; Buydens, L.M.C.

    2002-01-01

    This paper describes a technique to overcome the sensitivity of fuzzy C-means clustering for unequal cluster sizes in multivariate images. As FCM tends to balance the number of points in each cluster, cluster centres of smaller clusters are drawn to larger adjacent clusters. In order to overcome

  14. Multicriteria Personnel Selection by the Modified Fuzzy VIKOR Method

    Directory of Open Access Journals (Sweden)

    Rasim M. Alguliyev

    2015-01-01

    Full Text Available Personnel evaluation is an important process in human resource management. The multicriteria nature and the presence of both qualitative and quantitative factors make it considerably more complex. In this study, a fuzzy hybrid multicriteria decision-making (MCDM model is proposed to personnel evaluation. This model solves personnel evaluation problem in a fuzzy environment where both criteria and weights could be fuzzy sets. The triangular fuzzy numbers are used to evaluate the suitability of personnel and the approximate reasoning of linguistic values. For evaluation, we have selected five information culture criteria. The weights of the criteria were calculated using worst-case method. After that, modified fuzzy VIKOR is proposed to rank the alternatives. The outcome of this research is ranking and selecting best alternative with the help of fuzzy VIKOR and modified fuzzy VIKOR techniques. A comparative analysis of results by fuzzy VIKOR and modified fuzzy VIKOR methods is presented. Experiments showed that the proposed modified fuzzy VIKOR method has some advantages over fuzzy VIKOR method. Firstly, from a computational complexity point of view, the presented model is effective. Secondly, compared to fuzzy VIKOR method, it has high acceptable advantage compared to fuzzy VIKOR method.

  15. Multicriteria Personnel Selection by the Modified Fuzzy VIKOR Method.

    Science.gov (United States)

    Alguliyev, Rasim M; Aliguliyev, Ramiz M; Mahmudova, Rasmiyya S

    2015-01-01

    Personnel evaluation is an important process in human resource management. The multicriteria nature and the presence of both qualitative and quantitative factors make it considerably more complex. In this study, a fuzzy hybrid multicriteria decision-making (MCDM) model is proposed to personnel evaluation. This model solves personnel evaluation problem in a fuzzy environment where both criteria and weights could be fuzzy sets. The triangular fuzzy numbers are used to evaluate the suitability of personnel and the approximate reasoning of linguistic values. For evaluation, we have selected five information culture criteria. The weights of the criteria were calculated using worst-case method. After that, modified fuzzy VIKOR is proposed to rank the alternatives. The outcome of this research is ranking and selecting best alternative with the help of fuzzy VIKOR and modified fuzzy VIKOR techniques. A comparative analysis of results by fuzzy VIKOR and modified fuzzy VIKOR methods is presented. Experiments showed that the proposed modified fuzzy VIKOR method has some advantages over fuzzy VIKOR method. Firstly, from a computational complexity point of view, the presented model is effective. Secondly, compared to fuzzy VIKOR method, it has high acceptable advantage compared to fuzzy VIKOR method.

  16. A Fuzzy Co-Clustering approach for Clickstream Data Pattern

    CERN Document Server

    Rathipriya, R

    2011-01-01

    Web Usage mining is a very important tool to extract the hidden business intelligence data from large databases. The extracted information provides the organizations with the ability to produce results more effectively to improve their businesses and increasing of sales. Co-clustering is a powerful bipartition technique which identifies group of users associated to group of web pages. These associations are quantified to reveal the users' interest in the different web pages' clusters. In this paper, Fuzzy Co-Clustering algorithm is proposed for clickstream data to identify the subset of users of similar navigational behavior /interest over a subset of web pages of a website. Targeting the users group for various promotional activities is an important aspect of marketing practices. Experiments are conducted on real dataset to prove the efficiency of proposed algorithm. The results and findings of this algorithm could be used to enhance the marketing strategy for directing marketing, advertisements for web base...

  17. The Fuzzy Logic Method for Simpler Forecasting

    Directory of Open Access Journals (Sweden)

    Jeffrey E. Jarrett

    2011-08-01

    Full Text Available Fildes and Makridakis (1998, Makridakis and Hibon (2000, and Fildes (2001 indicate that simple extrapolative forecasting methods that are robust forecast equally as well or better than more complicated methods, i.e. Box-Jenkins and other methods. We study the Direct Set Assignment (DSA extrapolative forecasting method. The DSA method is a non-linear extrapolative forecasting method developed within the Mamdani Development Framework, and designed to mimic the architecture of a fuzzy logic control system. We combine the DSA method Winters' Exponential smoothing. This combination provides the best observed forecast accuracy in seven of nine subcategories of time series, and is the top three in terms of observed accuracy in two subcategories. Hence, fuzzy logic which is the basis of the DSA method often is the best method for forecasting.

  18. A Novel Multiresolution Fuzzy Segmentation Method on MR Image

    Institute of Scientific and Technical Information of China (English)

    ZHANG HongMei(张红梅); BIAN ZhengZhong(卞正中); YUAN ZeJian(袁泽剑); YE Min(叶敏); JI Feng(冀峰)

    2003-01-01

    Multiresolution-based magnetic resonance (MR) image segmentation has attractedattention for its ability to capture rich information across scales compared with the conventionalsegmentation methods. In this paper, a new scale-space-based segmentation model is presented,where both the intra-scale and inter-scale properties are considered and formulated as two fuzzyenergy functions. Meanwhile, a control parameter is introduced to adjust the contribution of thesimilarity character across scales and the clustering character within the scale. By minimizing thecombined inter/intra energy function, the multiresolution fuzzy segmentation algorithm is derived.Then the coarse to fine leading segmentation is performed automatically and iteratively on a set ofmultiresolution images. The validity of the proposed algorithm is demonstrated by the test imageand pathological MR images. Experiments show that by this approach the segmentation results,especially in the tumor area delineation, are more precise than those of the conventional fuzzy segmentation methods.

  19. Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Lingli Jiang

    2011-01-01

    Full Text Available This paper proposes a new approach combining autoregressive (AR model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.

  20. New judging model of fuzzy cluster optimal dividing based on rough sets theory

    Institute of Scientific and Technical Information of China (English)

    Wang Yun; Liu Qinghong; Mu Yong; Shi Kaiquan

    2007-01-01

    To investigate the judging problem of optimal dividing matrix among several fuzzy dividing matrices in fuzzy dividing space, correspondingly, which is determined by the various choices of cluster samples in the totality sample space, two algorithms are proposed on the basis of the data analysis method in rough sets theory: information system discrete algorithm (algorithm 1) and samples representatives judging algorithm (algorithm 2).On the principle of the farthest distance, algorithm 1 transforms continuous data into discrete form which could be transacted by rough sets theory.Taking the approximate precision as a criterion, algorithm 2 chooses the sample space with a good representative.Hence, the clustering sample set in inducing and computing optimal dividing matrix can be achieved.Several theorems are proposed to provide strict theoretic foundations for the execution of the algorithm model.An applied example based on the new algorithm model is given, whose result verifies the feasibility of this new algorithm model.

  1. Road Surface Modeling and Representation from Point Cloud Based on Fuzzy Clustering

    Institute of Scientific and Technical Information of China (English)

    ZHANG Yi; YAN Li

    2007-01-01

    A scheme for an automatic road surface modeling from a noisy point cloud is presented. The normal vectors of the point cloud are estimated by distance-weighted fitting of local plane. Then, an automatic recognition of the road surface from noise is performed based on the fuzzy clustering of normal vectors, with which the mean value is calculated and the projecting plane of point cloud is created to obtain the geometric model accordingly. Based on fuzzy clustering of the intensity attributed to each point, different objects on the road surface are assigned different colors for representing abundant appearances.This unsupervised method is demonstrated in the experiment and shows great effectiveness in reconstructing and rendering better road surface.

  2. Adomian Method for Solving Fuzzy Fredholm-Volterra Integral Equations

    Directory of Open Access Journals (Sweden)

    M. Barkhordari Ahmadi

    2013-09-01

    Full Text Available In this paper, Adomian method has been applied to approximate the solution of fuzzy volterra-fredholm integral equation. That, by using parametric form of fuzzy numbers, a fuzzy volterra-fredholm integral equation has been converted to a system of volterra-fredholm integral equation in crisp case. Finally, the method is explained with illustrative examples.

  3. Fuzzy clustering-based segmented attenuation correction in whole-body PET

    CERN Document Server

    Zaidi, H; Boudraa, A; Slosman, DO

    2001-01-01

    Segmented-based attenuation correction is now a widely accepted technique to reduce noise contribution of measured attenuation correction. In this paper, we present a new method for segmenting transmission images in positron emission tomography. This reduces the noise on the correction maps while still correcting for differing attenuation coefficients of specific tissues. Based on the Fuzzy C-Means (FCM) algorithm, the method segments the PET transmission images into a given number of clusters to extract specific areas of differing attenuation such as air, the lungs and soft tissue, preceded by a median filtering procedure. The reconstructed transmission image voxels are therefore segmented into populations of uniform attenuation based on the human anatomy. The clustering procedure starts with an over-specified number of clusters followed by a merging process to group clusters with similar properties and remove some undesired substructures using anatomical knowledge. The method is unsupervised, adaptive and a...

  4. Hesitant fuzzy sets theory

    CERN Document Server

    Xu, Zeshui

    2014-01-01

    This book provides the readers with a thorough and systematic introduction to hesitant fuzzy theory. It presents the most recent research results and advanced methods in the field. These includes: hesitant fuzzy aggregation techniques, hesitant fuzzy preference relations, hesitant fuzzy measures, hesitant fuzzy clustering algorithms and hesitant fuzzy multi-attribute decision making methods. Since its introduction by Torra and Narukawa in 2009, hesitant fuzzy sets have become more and more popular and have been used for a wide range of applications, from decision-making problems to cluster analysis, from medical diagnosis to personnel appraisal and information retrieval. This book offers a comprehensive report on the state-of-the-art in hesitant fuzzy sets theory and applications, aiming at becoming a reference guide for both researchers and practitioners in the area of fuzzy mathematics and other applied research fields (e.g. operations research, information science, management science and engineering) chara...

  5. An Airborne Radar Clutter Tracking Algorithm Based on Multifractal and Fuzzy C-Mean Cluster

    Institute of Scientific and Technical Information of China (English)

    Wei Zhang; Sheng-Lin Yu; Gong Zhang

    2007-01-01

    For an airborne lookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const false alarm rate (CFAR) schemes. In this work, clutter tracking is done in image domain and an algorithm combining multifractal and fuzzy C-mean (FCM) cluster is proposed. The clutter with large dynamic distributions in power density is converted to steady distributions of multifractal exponents by the multifractal transformation with the optimum moment. Then, later, the main lobe and side lobe are tracked from the multifractal exponents by FCM clustering method.

  6. Optimization method of multi-distribution center location based on fuzzy clustering algorithm%基于模糊聚类算法的多配送中心选址优化方法

    Institute of Scientific and Technical Information of China (English)

    毛海军; 王勇; 杭文; 于航; 何杰

    2012-01-01

    To optimize multi-distribution center location operation in two-level facilities logistics network, the main influential factors on locating distribution centers are extracted, and a comprehensive evaluation index system is set up. Firstly, the linguistic variables are represented by triangular fuzzy number to implement a comprehensive evaluation for candidate distribution centers. Secondly, the interval number priority degree function method is adopted to integrate the criteria index into the first criteria index, and the integrated project evaluation index value is used as the input of fuzzy clustering algorithm for clustering operation. The clustering validity index is designed to analyze the rationality of clustering results. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) method is used to rank the candidate distribution centers within the clustering unit, and the locations and quantities of distribution centers are determined. The results of an application example show that when the membership function value is 0.740 2, the clustering validity index gets the smallest value 2.43. The operation can divide the candidate distribution centers into four clusters and select the distribution center location in each cluster, making the location results reasonable and more advantageous than other methods. Therefore, the proposed method is more effective in addressing multi-distribution center location problem.%为了优化二级设施物流网络中多配送中心的选址操作,提取了影响配送中心选址的主要因素,建立了一种综合评价指标体系.首先,将语言变量值用三角模糊数表示,对备选配送中心进行综合评价;然后,采用区间数优度函数法将二级准则指标集成到一级准则指标上,以集成后的方案评价指标值作为模糊聚类算法的输入进行聚类操作,并设计了聚类有效性指标以用于判断聚类结果合理性;最后,应用TOPSIS方法对各

  7. Application of a New Fuzzy Clustering Algorithm in Intrusion Detection

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    This paper presents a new Section Set Adaptive FCM algorithm. The algorithm solved the shortcomings of localoptimality, unsure classification and clustering numbers ascertained previously. And it improved on the architecture of FCM al-gorithm, enhanced the analysis for effective clustering. During the clustering processing, it may adjust clustering numbers dy-namically. Finally, it used the method of section set decreasing the time of classification. By experiments, the algorithm can im-prove dependability of clustering and correctness of classification.

  8. CAF: Cluster algorithm and a-star with fuzzy approach for lifetime enhancement in wireless sensor networks

    KAUST Repository

    Yuan, Y.

    2014-04-28

    Energy is a major factor in designing wireless sensor networks (WSNs). In particular, in the real world, battery energy is limited; thus the effective improvement of the energy becomes the key of the routing protocols. Besides, the sensor nodes are always deployed far away from the base station and the transmission energy consumption is index times increasing with the increase of distance as well. This paper proposes a new routing method for WSNs to extend the network lifetime using a combination of a clustering algorithm, a fuzzy approach, and an A-star method. The proposal is divided into two steps. Firstly, WSNs are separated into clusters using the Stable Election Protocol (SEP) method. Secondly, the combined methods of fuzzy inference and A-star algorithm are adopted, taking into account the factors such as the remaining power, the minimum hops, and the traffic numbers of nodes. Simulation results demonstrate that the proposed method has significant effectiveness in terms of balancing energy consumption as well as maximizing the network lifetime by comparing the performance of the A-star and fuzzy (AF) approach, cluster and fuzzy (CF)method, cluster and A-star (CA)method, A-star method, and SEP algorithm under the same routing criteria. 2014 Yali Yuan et al.

  9. A Semantic Retrieval Method Based on the Fuzzy Reasoning

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    This paper gives a semantic fuzzy retrieval method of multimedia object,discusses the principle of fuzzy semantic retrieval technique,presents a fuzzy reasoning mechanism based on the knowledge base,and designs the relevant reasoning algorithms.Researchful results have innovative significance.

  10. Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach

    Science.gov (United States)

    Julie, E. Golden; Selvi, S. Tamil

    2016-01-01

    Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes. PMID:26881269

  11. Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach.

    Science.gov (United States)

    Julie, E Golden; Selvi, S Tamil

    2016-01-01

    Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.

  12. Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach

    Directory of Open Access Journals (Sweden)

    E. Golden Julie

    2016-01-01

    Full Text Available Wireless sensor networks (WSNs consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.

  13. 模糊WINGS视角下的ANP加权矩阵新构造方法%New construction method for ANP cluster-weighted matrix from view of fuzzy WINGS

    Institute of Scientific and Technical Information of China (English)

    孙永河; 李春好; 谢晖; 段万春

    2014-01-01

    构造因素集加权矩阵是ANP系统未加权超矩阵到加权超矩阵转化的一个关键技术。然而从已有的三种构造方法看,两两比较法比较机理混乱,而等权矩阵假设构造法通常是无效的,并且基于DEMATEL(决策试行与评价实验室)的构造方法不仅存在忽视因素集自身强度的内在不足,而且也难以反映专家在对因素集之间影响关系判断时存在的“不精确性”。为克服上述缺陷,提出一种模糊WINGS(加权影响情景下的非线性测度体系)视角下的ANP因素集加权矩阵新构造方法。该方法一方面给出改进后的模糊DELPHI决策程序,充分考虑了专家判断过程中的“不精确性”。另一方面,系统提出模糊WINGS的方法思路,在系统因素集影响关系判别时充分考虑了因素集的自我影响强度,使因素集直接影响矩阵的构造更为合理。实例对比验证结果表明,该方法是科学合理的,有着较强的实践应用可操作性。%It is vital to construct a Cluster-Weighted Matrix(CWM)among clusters when converting an unweighted super-matrix of ANP(Analytic Network Process)system to the weighted supermatrix. However, existing three construction methods have the following disadvantages. First, pairwise comparison method has disordered comparison mechanism. Second, the method of supposing equal weight matrix is usually inefficient unless the CWM is a nominal matrix. Third, the construction method based on DEMATEL(Decision Making Trial and Evaluation Lab)not only ignores the Impor-tance of Each Cluster(IEC), also is the inaccuracy of expert judgments overlooked. To overcome the above drawbacks, this paper suggests a new fuzzy WINGS(Weighted Influence Non-linear Gauge System)to construct CWM of ANP. On one hand, in the new approach, the inaccuracy of expert judgments is considered by improved fuzzy DELPHI procedures. On the other hand, the idea of fuzzy WINGS is suggested in

  14. An efficient algorithm for automatically generating multivariable fuzzy systems by Fourier series method.

    Science.gov (United States)

    Chen, Liang; Tokuda, N

    2002-01-01

    By exploiting the Fourier series expansion, we have developed a new constructive method of automatically generating a multivariable fuzzy inference system from any given sample set with the resulting multivariable function being constructed within any specified precision to the original sample set. The given sample sets are first decomposed into a cluster of simpler sample sets such that a single input fuzzy system is constructed readily for a sample set extracted directly from the cluster independent of the other variables. Once the relevant fuzzy rules and membership functions are constructed for each of the variables completely independent of the other variables, the resulting decomposed fuzzy rules and membership functions are integrated back into the fuzzy system appropriate for the original sample set requiring only a moderate cost of computation in the required decomposition and composition processes. After proving two basic theorems which we need to ensure the validity of the decomposition and composition processes of the system construction, we have demonstrated a constructive algorithm of a multivariable fuzzy system. Exploiting an implicit error bound analysis available at each of the construction steps, the present Fourier method is capable of implementing a more stable fuzzy system than the power series expansion method of ParNeuFuz and PolyNeuFuz, covering and implementing a wider range of more robust applications.

  15. PHONETIC CLASSIFICATION BY ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM AND SUBTRACTIVE CLUSTERING

    Directory of Open Access Journals (Sweden)

    Samiya Silarbi

    2014-09-01

    Full Text Available This paper presents the application of Adaptive Network Based Fuzzy Inference System ANFIS on speech recognition. The primary tasks of fuzzy modeling are structure identification and parameter optimization, the former determines the numbers of membership functions and fuzzy if-then rules while the latter identifies a feasible set of parameters under the given structure. However, the increase of input dimension, rule numbers will have an exponential growth and there will cause problem of “rule disaster”. Thus, determination of an appropriate structure becomes an important issue where subtractive clustering is applied to define an optimal initial structure and obtain small number of rules. The appropriate learning algorithm is performed on TIMIT speech database supervised type, a pre-processing of the acoustic signal and extracting the coefficients MFCCs parameters relevant to the recognition system. Finally, hybrid learning combines the gradient decent and least square estimation LSE of parameters network. The results obtained show the effectiveness of the method in terms of recognition rate and number of fuzzy rules generated.

  16. Hesitant fuzzy methods for multiple criteria decision analysis

    CERN Document Server

    Zhang, Xiaolu

    2017-01-01

    The book offers a comprehensive introduction to methods for solving multiple criteria decision making and group decision making problems with hesitant fuzzy information. It reports on the authors’ latest research, as well as on others’ research, providing readers with a complete set of decision making tools, such as hesitant fuzzy TOPSIS, hesitant fuzzy TODIM, hesitant fuzzy LINMAP, hesitant fuzzy QUALIFEX, and the deviation modeling approach with heterogeneous fuzzy information. The main focus is on decision making problems in which the criteria values and/or the weights of criteria are not expressed in crisp numbers but are more suitable to be denoted as hesitant fuzzy elements. The largest part of the book is devoted to new methods recently developed by the authors to solve decision making problems in situations where the available information is vague or hesitant. These methods are presented in detail, together with their application to different type of decision-making problems. All in all, the book ...

  17. Unconventional methods for clustering

    Science.gov (United States)

    Kotyrba, Martin

    2016-06-01

    Cluster analysis or clustering is a task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is the main task of exploratory data mining and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. The topic of this paper is one of the modern methods of clustering namely SOM (Self Organising Map). The paper describes the theory needed to understand the principle of clustering and descriptions of algorithm used with clustering in our experiments.

  18. A new fuzzy Monte Carlo method for solving SLAE with ergodic fuzzy Markov chains

    Directory of Open Access Journals (Sweden)

    Maryam Gharehdaghi

    2015-05-01

    Full Text Available In this paper we introduce a new fuzzy Monte Carlo method for solving system of linear algebraic equations (SLAE over the possibility theory and max-min algebra. To solve the SLAE, we first define a fuzzy estimator and prove that this is an unbiased estimator of the solution. To prove unbiasedness, we apply the ergodic fuzzy Markov chains. This new approach works even for cases with coefficients matrix with a norm greater than one.

  19. Joint inversion of multiple geophysical and petrophysical data using generalized fuzzy clustering algorithms

    Science.gov (United States)

    Sun, Jiajia; Li, Yaoguo

    2017-02-01

    Joint inversion that simultaneously inverts multiple geophysical data sets to recover a common Earth model is increasingly being applied to exploration problems. Petrophysical data can serve as an effective constraint to link different physical property models in such inversions. There are two challenges, among others, associated with the petrophysical approach to joint inversion. One is related to the multimodality of petrophysical data because there often exist more than one relationship between different physical properties in a region of study. The other challenge arises from the fact that petrophysical relationships have different characteristics and can exhibit point, linear, quadratic, or exponential forms in a crossplot. The fuzzy c-means (FCM) clustering technique is effective in tackling the first challenge and has been applied successfully. We focus on the second challenge in this paper and develop a joint inversion method based on variations of the FCM clustering technique. To account for the specific shapes of petrophysical relationships, we introduce several different fuzzy clustering algorithms that are capable of handling different shapes of petrophysical relationships. We present two synthetic and one field data examples and demonstrate that, by choosing appropriate distance measures for the clustering component in the joint inversion algorithm, the proposed joint inversion method provides an effective means of handling common petrophysical situations we encounter in practice. The jointly inverted models have both enhanced structural similarity and increased petrophysical correlation, and better represent the subsurface in the spatial domain and the parameter domain of physical properties.

  20. Prediction of Adsorption of Cadmium by Hematite Using Fuzzy C-Means Clustering Technique

    Directory of Open Access Journals (Sweden)

    Sriparna Das

    2012-11-01

    Full Text Available Clustering is partitioning of data set into subsets (clusters, so that the data in each subset share some common trait. In this paper, an algorithm has been proposed based on Fuzzy C-means clustering technique for prediction of adsorption of cadmium by hematite. The original data elements have been used for clustering the random data set. The random data have been generated within the minimum and maximum value of test data. The proposed algorithm has been applied on random dataset considering the original data set as initial cluster center. A threshold value has been taken to make the boundary around the clustering center. Finally, after execution of algorithm, modified cluster centers have been computed based on each initial cluster center. The modified cluster centers have been treated as predicted data set. The algorithm has been tested in prediction of adsorption of cadmium by hematite. The error has been calculated between the original data and predicted data. It has been observed that the proposed algorithm has given better result than the previous applied methods.

  1. Comparison of fuzzy AHP and fuzzy TODIM methods for landfill location selection.

    Science.gov (United States)

    Hanine, Mohamed; Boutkhoum, Omar; Tikniouine, Abdessadek; Agouti, Tarik

    2016-01-01

    Landfill location selection is a multi-criteria decision problem and has a strategic importance for many regions. The conventional methods for landfill location selection are insufficient in dealing with the vague or imprecise nature of linguistic assessment. To resolve this problem, fuzzy multi-criteria decision-making methods are proposed. The aim of this paper is to use fuzzy TODIM (the acronym for Interactive and Multi-criteria Decision Making in Portuguese) and the fuzzy analytic hierarchy process (AHP) methods for the selection of landfill location. The proposed methods have been applied to a landfill location selection problem in the region of Casablanca, Morocco. After determining the criteria affecting the landfill location decisions, fuzzy TODIM and fuzzy AHP methods are applied to the problem and results are presented. The comparisons of these two methods are also discussed.

  2. Using genetic algorithm based fuzzy adaptive resonance theory for clustering analysis

    Institute of Scientific and Technical Information of China (English)

    LIU Bo; WANG Yong; WANG Hong-jian

    2006-01-01

    In the clustering applications field, fuzzy adaptive resonance theory system has been widely applied. But, three parameters of fuzzy adaptive resonance theory need to be adjusted manually for obtaining better clustering. It needs much time to test and does not assure a best result. Genetic algorithm is an optimal mathematical search technique based on the principles of natural selection and genetic recombination. So, to make the fuzzy adaptive resonance theory parameters choosing process automation, an approach incorporating genetic algorithm and fuzzy adaptive resonance theory neural network has been applied. Then, the best clustering result can be obtained.Through experiment, it can be proved that the most appropriate parameters of fuzzy adaptive resonance theory can be gained effectively by this approach.

  3. Fuzzy clustering: critical analysis of the contextual mechanisms employed by three neural network models

    Science.gov (United States)

    Baraldi, Andrea; Parmiggiani, Flavio

    1996-06-01

    According to the following definition, taken from the literature, a fuzzy clustering mechanism allows the same input pattern to belong to multiple categories to different degrees. Many clustering neural network (NN) models claim to feature fuzzy properties, but several of them (like the Fuzzy ART model) do not satisfy this definition. Vice versa, we believe that Kohonen's Self-Organizing Map, SOM, satisfies the definition provided above, even though this NN model is well-known to (robustly) perform topologically ordered mapping rather than fuzzy clustering. This may sound as a paradox if we consider that several fuzzy NN models (such as the Fuzzy Learning Vector Quantization, FLVQ, which was first called Fuzzy Kohonen Clustering Network, FKCN) were originally developed to enhance Kohonen's models (such as SOM and the vector quantization model, VQ). The fuzziness of SOM indicates that a network of processing elements (PEs) can verify the fuzzy clustering definition when it exploits local rules which are biologically plausible (such as the Kohonen bubble strategy). This is equivalent to state that the exploitation of the fuzzy set theory in the development of complex systems (e.g., clustering NNs) may provide new mathematical tools (e.g., the definition of membership function) to simulate the behavior of those cooperative/competitive mechanisms already identified by neurophysiological studies. When a biologically plausible cooperative/competitive strategy is pursued effectively, neighboring PEs become mutually coupled to gain sensitivity to contextual effects. PEs which are mutually coupled are affected by vertical (inter-layer) as well as horizontal (intra-layer) connections. To summarize, we suggest to relate the study of fuzzy clustering mechanisms to the multi-disciplinary science of complex systems, with special regard to the investigation of the cooperative/competitive local rules employed by complex systems to gain sensitivity to contextual effects in

  4. Classification of posture maintenance data with fuzzy clustering algorithms

    Science.gov (United States)

    Bezdek, James C.

    1992-01-01

    Sensory inputs from the visual, vestibular, and proprioreceptive systems are integrated by the central nervous system to maintain postural equilibrium. Sustained exposure to microgravity causes neurosensory adaptation during spaceflight, which results in decreased postural stability until readaptation occurs upon return to the terrestrial environment. Data which simulate sensory inputs under various sensory organization test (SOT) conditions were collected in conjunction with Johnson Space Center postural control studies using a tilt-translation device (TTD). The University of West Florida applied the fuzzy c-meams (FCM) clustering algorithms to this data with a view towards identifying various states and stages of subjects experiencing such changes. Feature analysis, time step analysis, pooling data, response of the subjects, and the algorithms used are discussed.

  5. Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs

    Directory of Open Access Journals (Sweden)

    Theis Fabian J

    2010-10-01

    Full Text Available Abstract Background Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of k-partite graphs. These graphs contain k different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type. Results Since entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a k-partite graph partitioning algorithm that allows for overlapping (fuzzy clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted k-partite graph that connects the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2. Conclusions In order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy k-partite graph partitioning

  6. New two-dimensional fuzzy C-means clustering algorithm for image segmentation

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation,a novel two-dimensional FCM clustering algorithm for image segmentation was proposed.In this method,the image segmentation was converted into an optimization problem.The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixcls described by the improved two-dimensional histogram.By making use of the global searching ability of the predator-prey particle swarm optimization,the optimal cluster center could be obtained by iterative optimization,and the image segmentation could be accomplished.The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%.The proposed algorithm has strong anti-noise capability,high clustering accuracy and good segment effect,indicating that it is an effective algorithm for image segmentation.

  7. Fault Diagnosis and Reliability Analysis Using Fuzzy Logic Method

    Institute of Scientific and Technical Information of China (English)

    Miao Zhinong; Xu Yang; Zhao Xiangyu

    2006-01-01

    A new fuzzy logic fault diagnosis method is proposed. In this method, fuzzy equations are employed to estimate the component state of a system based on the measured system performance and the relationship between component state and system performance which is called as "performance-parameter" knowledge base and constructed by expert. Compared with the traditional fault diagnosis method, this fuzzy logic method can use humans intuitive knowledge and dose not need a precise mapping between system performance and component state. Simulation proves its effectiveness in fault diagnosis. Then, the reliability analysis is performed based on the fuzzy logic method.

  8. A fuzzy method to learn text classifier from labeled and unlabeled examples

    Institute of Scientific and Technical Information of China (English)

    刘宏; 黄上腾

    2004-01-01

    In text classification, labeling documents is a tedious and costly task, as it would consume a lot of expert time. On the other hand, it usually is easier to obtain a lot of unlabeled documents, with the help of some tools like Digital Library, Crawler Programs, and Searching Engine. To learn text classifier from labeled and unlabeled examples, a novel fuzzy method is proposed. Firstly, a Seeded Fuzzy c-means Clustering algorithm is proposed to learn fuzzy clusters from a set of labeled and unlabeled examples. Secondly, based on the resulting fuzzy clusters, some examples with high confidence are selected to construct training data set. Finally,the constructed training data set is used to train Fuzzy Support Vector Machine, and get text classifier. Empirical results on two benchmark datasets indicate that, by incorporating unlabeled examples into learning process,the method performs significantly better than FSVM trained with a small number of labeled examples only. Also, the method proposed performs at least as well as the related method-EM with Naive Bayes. One advantage of the method proposed is that it does not rely on any parametric assumptions about the data as it is usually the case with generative methods widely used in semi-supervised learning.

  9. A proposed method for solving fuzzy system of linear equations.

    Science.gov (United States)

    Kargar, Reza; Allahviranloo, Tofigh; Rostami-Malkhalifeh, Mohsen; Jahanshaloo, Gholam Reza

    2014-01-01

    This paper proposes a new method for solving fuzzy system of linear equations with crisp coefficients matrix and fuzzy or interval right hand side. Some conditions for the existence of a fuzzy or interval solution of m × n linear system are derived and also a practical algorithm is introduced in detail. The method is based on linear programming problem. Finally the applicability of the proposed method is illustrated by some numerical examples.

  10. Analysis of construction dynamic plan using fuzzy critical path method

    Directory of Open Access Journals (Sweden)

    Kurij Kazimir V.

    2014-01-01

    Full Text Available Critical Path Method (CPM technique has become widely recognized as valuable tool for the planning and scheduling large construction projects. The aim of this paper is to present an analytical method for finding the Critical Path in the precedence network diagram where the duration of each activity is represented by a trapezoidal fuzzy number. This Fuzzy Critical Path Method (FCPM uses a defuzzification formula for trapezoidal fuzzy number and applies it on the total float (slack time for each activity in the fuzzy precedence network to find the critical path. The method presented in this paper is very effective in determining the critical activities and finding the critical paths.

  11. Personnel Selection Based on Fuzzy Methods

    Directory of Open Access Journals (Sweden)

    Lourdes Cañós

    2011-03-01

    Full Text Available The decisions of managers regarding the selection of staff strongly determine the success of the company. A correct choice of employees is a source of competitive advantage. We propose a fuzzy method for staff selection, based on competence management and the comparison with the valuation that the company considers the best in each competence (ideal candidate. Our method is based on the Hamming distance and a Matching Level Index. The algorithms, implemented in the software StaffDesigner, allow us to rank the candidates, even when the competences of the ideal candidate have been evaluated only in part. Our approach is applied in a numerical example.

  12. Triple I method and interval valued fuzzy reasoning

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The aims of this paper are: (i) to show that the CRI method should be improved and remould into the triple I method, (ii) to propose a new type of fuzzy reasoning with multiple rules of which the premise of each rule is an interval valued fuzzy subset, (iii) to establish the "fire one or leave (FOOL)" principle as pretreatment for solving the fuzzy reasoning problem mentioned in (ii), and (iv) to solve the problem mentioned in (ii).

  13. Triple I method and interval valued fuzzy reasoning

    Institute of Scientific and Technical Information of China (English)

    王国俊

    2000-01-01

    The aims of this paper are.- (i) to show that the CRI method should be improved and remould into the triple I method, (ii) to propose a new type of fuzzy reasoning with multiple rules of which the premise of each rule is an interval valued fuzzy subset, (iii) to establish the "fire one or leave (FOOL)" principle as pretreatment for solving the fuzzy reasoning problem mentioned in (ii), and (iv) to solve the problem mentioned in (ii).

  14. Self-organizing fuzzy clustering neural network and application to electronic countermeasures effectiveness evaluation

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    A self-organizing fuzzy clustering neural network by combining the self-organizing Kohonen clustering network with the fuzzy theory is proposed.This network model is designed for the effectiveness evaluation of electronic countermeasures,which not only exerts the advantages of the fuzzy theory,but also has a good ability in machine learning and data analysis.The subjective value of sample versus class is computed by the fuzzy computing theory,and the classified results obtained by self-organizing learning of Kohonen neural network are represented on output layer.Meanwhile,the fuzzy competition learning algorithm keeps the similar information between samples and overcomes the disadvantages of neural network which has fewer samples.The simulation result indicates that the proposed algorithm is feasible and effective.

  15. Fuzzy Neuron: Method and Hardware Realization

    Science.gov (United States)

    Krasowski, Michael J.; Prokop, Norman F.

    2014-01-01

    This innovation represents a method by which single-to-multi-input, single-to-many-output system transfer functions can be estimated from input/output data sets. This innovation can be run in the background while a system is operating under other means (e.g., through human operator effort), or may be utilized offline using data sets created from observations of the estimated system. It utilizes a set of fuzzy membership functions spanning the input space for each input variable. Linear combiners associated with combinations of input membership functions are used to create the output(s) of the estimator. Coefficients are adjusted online through the use of learning algorithms.

  16. Performance Evaluation of K-Mean and Fuzzy C-Mean Image Segmentation Based Clustering Classifier

    Directory of Open Access Journals (Sweden)

    Hind R.M Shaaban

    2015-12-01

    Full Text Available This paper presents Evaluation K-mean and Fuzzy c-mean image segmentation based Clustering classifier. It was followed by thresholding and level set segmentation stages to provide accurate region segment. The proposed stay can get the benefits of the K-means clustering. The performance and evaluation of the given image segmentation approach were evaluated by comparing K-mean and Fuzzy c-mean algorithms in case of accuracy, processing time, Clustering classifier, and Features and accurate performance results. The database consists of 40 images executed by K-mean and Fuzzy c-mean image segmentation based Clustering classifier. The experimental results confirm the effectiveness of the proposed Fuzzy c-mean image segmentation based Clustering classifier. The statistical significance Measures of mean values of Peak signal-to-noise ratio (PSNR and Mean Square Error (MSE and discrepancy are used for Performance Evaluation of K-mean and Fuzzy c-mean image segmentation. The algorithm’s higher accuracy can be found by the increasing number of classified clusters and with Fuzzy c-mean image segmentation.

  17. Supply chain management under fuzziness recent developments and techniques

    CERN Document Server

    Öztayşi, Başar

    2014-01-01

    Supply Chain Management Under Fuzziness presents recently developed fuzzy models and techniques for supply chain management. These include: fuzzy PROMETHEE, fuzzy AHP, fuzzy ANP, fuzzy VIKOR, fuzzy DEMATEL, fuzzy clustering, fuzzy linear programming, and fuzzy inference systems. The book covers both practical applications and new developments concerning these methods. This book offers an excellent resource for researchers and practitioners in supply chain management and logistics, and will provide them with new suggestions and directions for future research. Moreover, it will support graduate students in their university courses, such as specialized courses on supply chains and logistics, as well as related courses in the fields of industrial engineering, engineering management and business administration.

  18. An improved approach based on fuzzy clustering and Back-Propagation Neural Networks with adaptive learning rate for sales forecasting: Case study of PCB industry

    Directory of Open Access Journals (Sweden)

    Attariuas Hicham

    2012-05-01

    Full Text Available This paper describes new hybrid sales forecasting system based on fuzzy clustering and Back-propagation (BP Neural Networks with adaptive learning rate (FCBPN.The proposed approach is composed of three stages: (1 Winters Exponential Smoothing method will be utilized to take the trend effect into consideration; (2 utilizing Fuzzy C-Means clustering method (Used in an clusters memberships fuzzy system (CMFS, the clusters membership levels of each normalized data records will be extracted; (3 Each cluster will be fed into parallel BP networks with a learning rate adapted as the level of cluster membership of training data records. Compared to many researches which use Hard clustering, we employ fuzzy clustering which permits each data record to belong to each cluster to a certain degree, which allows the clusters to be larger which consequently increases the accuracy of the proposed forecasting system . Printed Circuit Board (PCB will be used as a case study to evaluate the precision of our proposed architecture. Experimental results show that the proposed model outperforms the previous and traditional approaches. Therefore, it is a very promising solution for industrial forecasting.

  19. Fuzzy C-Means Clustering and Energy Efficient Cluster Head Selection for Cooperative Sensor Network

    Science.gov (United States)

    Bhatti, Dost Muhammad Saqib; Saeed, Nasir; Nam, Haewoon

    2016-01-01

    We propose a novel cluster based cooperative spectrum sensing algorithm to save the wastage of energy, in which clusters are formed using fuzzy c-means (FCM) clustering and a cluster head (CH) is selected based on a sensor’s location within each cluster, its location with respect to fusion center (FC), its signal-to-noise ratio (SNR) and its residual energy. The sensing information of a single sensor is not reliable enough due to shadowing and fading. To overcome these issues, cooperative spectrum sensing schemes were proposed to take advantage of spatial diversity. For cooperative spectrum sensing, all sensors sense the spectrum and report the sensed energy to FC for the final decision. However, it increases the energy consumption of the network when a large number of sensors need to cooperate; in addition to that, the efficiency of the network is also reduced. The proposed algorithm makes the cluster and selects the CHs such that very little amount of network energy is consumed and the highest efficiency of the network is achieved. Using the proposed algorithm maximum probability of detection under an imperfect channel is accomplished with minimum energy consumption as compared to conventional clustering schemes. PMID:27618061

  20. Medical Image Segmentation Using Independent Component Analysis-Based Kernelized Fuzzy c-Means Clustering

    Directory of Open Access Journals (Sweden)

    Yao-Tien Chen

    2017-01-01

    Full Text Available Segmentation of brain tissues is an important but inherently challenging task in that different brain tissues have similar grayscale values and the intensity of a brain tissue may be confused with that of another one. The paper accordingly develops an ICKFCM method based on kernelized fuzzy c-means clustering with ICA analysis for extracting regions of interest in MRI brain images. The proposed method first removes the skull region using a skull stripping algorithm. Through ICA, three independent components are then extracted from multimodal medical images containing T1-weighted, T2-weighted, and PD-weighted MRI images. As MRI signals can be regarded as a combination of the signals from brain matters, ICA can be used for contrast enhancement of MRI images. Finally, the three independent components are utilized as inputs by KFCM algorithm to extract different brain tissues. Relying on the decomposition of a multivariate signal into independent non-Gaussian components and using a more appropriate kernel-induced distance for fuzzy clustering, the proposed method is capable of achieving greater reliability in both theory and practice than other segmentation approaches. According to the experiment results, the proposed method is capable of accurately extracting the complicated shapes of brain tissues and still remaining robust against various types of noises.

  1. A Novel Model of IDS Based on Fuzzy Cluster and Immune Principle

    Institute of Scientific and Technical Information of China (English)

    TAO Xin-min; LIU Fu-rong

    2005-01-01

    This paper presents a novel intrusion detection model based on fuzzy cluster and immune principle. The original rival penalized competitive learning (RPCL) algorithm is modified in order to address the problem of different variability of variables and correlation between variables, the sensitivity to initial number of clusters is also solved. Especially, we use the extended RPCL algorithm to determine the initial number of clusters in the fuzzy cluster algorithm. The genetic algorithm is used to optimize the radius deviation for the determination of characteristic function of abnormal subspace.

  2. A Robust Tolerance Design Method Based on Fuzzy Quality Loss

    Institute of Scientific and Technical Information of China (English)

    CAO Yan-long; MAO Jian; YANG Jiang-xin; WU Zhao-tong; WU Li-qun

    2006-01-01

    The traditional tolerance design model ignores the impact of noise factor,so that the design may be infeasible due to variations in design constraints.Based on the analysis of fuzzy factors in tolerance design and the limitations ofthe traditional Taguchi squared quality loss function,a fuzzy quality loss function model utilizing fuzzy theory was introduced.Concepts on fuzzy quality loss and fuzzy quality loss cost were proposed in the model.The characteristics of the new model and the advantages over the traditional Taguchi quality loss function were analyzed.A robust tolerance design model using a fuzzy quality loss function was proposed.An example was given to illustrate the proposed model.Results and comparisons show that the method is suitable and reliable,and makes the conclusions more objective and reasonable.

  3. Fuzzy Control Method with Application for Functional Neuromuscular Stimulation System

    Institute of Scientific and Technical Information of China (English)

    吴怀宇; 周兆英; 熊沈蜀

    2001-01-01

    A fuzzy control technique is applied to a functional neuromuscular stimulation (FNS) physicalmultiarticular muscle control system. The FNS multiarticular muscle control system based on the fuzzy controllerwas developed with the fuzzy control rule base. Simulation experiments were then conducted for the joint angletrajectories of both the elbow flexion and the wrist flexion using the proposed fuzzy control algorithm and aconventional PID control algorithm with the FNS physical multiarticular muscle control system. The simulationresults demonstrated that the proposed fuzzy control method is more suitable for the physiologicalcharacteristics than conventional PID control. In particular, both the trajectory-following and the stability of theFNS multiarticular muscle control system were greatly improved. Furthermore, the stimulating pulse trainsgenerated by the fuzzy controller were stable and smooth.``

  4. Reverse triple I method of restriction for fuzzy reasoning

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    A theory of reverse triple I method of restriction for implication operator R0 is proposed.And the general computation formulas of infimum for fuzzy modus ponens and supremum for fuzzy modus tollens of a-reverse triple I method of restriction are obtained respectively.

  5. Fuzzy Assessment Method and Its Application to Selecting Project Managers

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Open competition is a new form of the assessment of candidates and selection of project managers. This has many merits compared to the traditional administrative method of appointment. This article introduces a method of fuzzy assessment of project manager candidates. Fuzzy assessment unifies objective qualitative and quantitative appraisal and can be used for improving decision-making in the selection process.

  6. Mehar Methods for Fuzzy Optimal Solution and Sensitivity Analysis of Fuzzy Linear Programming with Symmetric Trapezoidal Fuzzy Numbers

    Directory of Open Access Journals (Sweden)

    Sukhpreet Kaur Sidhu

    2014-01-01

    Full Text Available The drawbacks of the existing methods to obtain the fuzzy optimal solution of such linear programming problems, in which coefficients of the constraints are represented by real numbers and all the other parameters as well as variables are represented by symmetric trapezoidal fuzzy numbers, are pointed out, and to resolve these drawbacks, a new method (named as Mehar method is proposed for the same linear programming problems. Also, with the help of proposed Mehar method, a new method, much easy as compared to the existing methods, is proposed to deal with the sensitivity analysis of the same type of linear programming problems.

  7. A Fuzzy Neural Network Based on Non-Euclidean Distance Clustering for Quality Index Model in Slashing Process

    Directory of Open Access Journals (Sweden)

    Yuxian Zhang

    2015-01-01

    Full Text Available The quality index model in slashing process is difficult to build by reason of the outliers and noise data from original data. To the above problem, a fuzzy neural network based on non-Euclidean distance clustering is proposed in which the input space is partitioned into many local regions by the fuzzy clustering based on non-Euclidean distance so that the computation complexity is decreased, and fuzzy rule number is determined by validity function based on both the separation and the compactness among clusterings. Then, the premise parameters and consequent parameters are trained by hybrid learning algorithm. The parameters identification is realized; meanwhile the convergence condition of consequent parameters is obtained by Lyapunov function. Finally, the proposed method is applied to build the quality index model in slashing process in which the experimental data come from the actual slashing process. The experiment results show that the proposed fuzzy neural network for quality index model has lower computation complexity and faster convergence time, comparing with GP-FNN, BPNN, and RBFNN.

  8. 纵横交叉算法与模糊聚类相结合的变压器故障诊断%Fault diagnosis method of transformer based on crisscross optimization algorithm and fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    孟安波; 卢海明; 郭壮志

    2016-01-01

    Optimized the FCM clustering by the proposed CSO ( CSO-FCM) is introduced to diagnose the fault of transformer in order to conquer the shortages of FCM clustering.The combination of dissolved gas analysis and FCM clustering is effective on improving the accuracy rate of power transformer fault diagnosis, but the result of FCM cluste-ring is unstable and easy getting stuck in a local optimum.The CSO algorithm includes horizon cross as well as verti-cal cross, whose combining can enhance the global convergent ability while the introduction of competitive mechanism drives the potential solutions approximate the global optima in an accelerating fashion without sacrificing the conver-gence speed.This novel method effectively compensates the demerits of single intelligent algorithm, which not only has the ability to dispose the unstable information of fuzzy theory, also has an advantage of global convergence of CSO. Simulation and case analysis indicate that, compared with the traditional FCM clustering, the CSO-FCM clustering can obtain high performance clustering center and effectively raise the accuracy and diagnosis speed of power transformer fault diagnosis.%针对FCM(模糊C-均值聚类)在变压器故障诊断中的不足,提出采用纵横交叉算法优化FCM ( CSO-FCM)聚类来进行故障诊断。溶解气体分析与FCM相结合,能有效提高变压器故障诊断的准确率,但FCM存在聚类结果不稳定和容易陷入局部最优等问题。而纵横交叉算法是一种基于种群的随机搜索算法,在算法中首次提出了维局部最优概念和纵横交叉双搜索思想。实验证明,相比其它主流群智能优化算法,CSO算法在解决维数灾问题和收敛精度问题方面取得了较大突破,能有效克服局部最优的问题。新诊断模型有效弥补了单一诊断法的不足,拥有全局收敛性强和处理模糊信息的能力。实例分析表明,该方法与传统FCM相比,能获得

  9. 利用模糊ISODATA聚类方法确定注采有效性%Determination of injection-production efficiency using ISODATA fuzzy clustering analysis method

    Institute of Scientific and Technical Information of China (English)

    王硕亮; 姜汉桥; 李俊键; 丁帅伟; 刘乐; 胡景宏

    2011-01-01

    the low efficiency circulation of injected water in formation has seriously effected oil fields efficient development at the late development stage with high water cut. Profile control and water shutoff is an important measure for solving the low efficiency cycle of injected water, and how to select the favorable wells whose injected water cycles inefficiently is the key issue for water shut off and profile control. The present decision-making theory of Profile control and water shutoff disturbed by many artificial factors such as index weight and index boundary in RE decision, so how to judge the wells whose injected water circulation is inefficiently based on the existed preliminary information is the key point. This paper proposes a indicator system for judging low efficient cycle, and then determines whether there is a low efficient cycle and the level of the inefficient according to different dynamic data characteristics using ISODATA cluster analysis method. For the wells that is in invalid cycle be required to take the water profile control and water shutoff decision while the wells in low efficient cycle be suggested a further observation. Thus, this method provides a basis for choosing wells to conduct the profile control and water shutoff technology.%高含水油田进入开发后期,注入水多在地层中低效循环,严重影响油田的高效开发.有效解决注入水低效循环的重要措施是调剖堵水,而调剖堵水决策的关键问题就是如何选井.目前的调剖堵水决策理论人为因素干扰较多(如RE决策中的指标权重和指标界限等),如何根据目前的基本认识判断出现低效循环的油水井成为一个关键问题.提出了一套适合于判断低效循环场的指标体系,然后利用ISODATA聚类分析方法,根据油水井不同的动态数据特征,判断每个并组是否出现了低效循环以及低效循环的级别,对已处于无效循环的井,建议进行调剖堵水,对处于低效循环

  10. Determining the number of clusters for kernelized fuzzy C-means algorithms for automatic medical image segmentation

    Directory of Open Access Journals (Sweden)

    E.A. Zanaty

    2012-03-01

    Full Text Available In this paper, we determine the suitable validity criterion of kernelized fuzzy C-means and kernelized fuzzy C-means with spatial constraints for automatic segmentation of magnetic resonance imaging (MRI. For that; the original Euclidean distance in the FCM is replaced by a Gaussian radial basis function classifier (GRBF and the corresponding algorithms of FCM methods are derived. The derived algorithms are called as the kernelized fuzzy C-means (KFCM and kernelized fuzzy C-means with spatial constraints (SKFCM. These methods are implemented on eighteen indexes as validation to determine whether indexes are capable to acquire the optimal clusters number. The performance of segmentation is estimated by applying these methods independently on several datasets to prove which method can give good results and with which indexes. Our test spans various indexes covering the classical and the rather more recent indexes that have enjoyed noticeable success in that field. These indexes are evaluated and compared by applying them on various test images, including synthetic images corrupted with noise of varying levels, and simulated volumetric MRI datasets. Comparative analysis is also presented to show whether the validity index indicates the optimal clustering for our datasets.

  11. Fuzzy forecasting based on fuzzy-trend logical relationship groups.

    Science.gov (United States)

    Chen, Shyi-Ming; Wang, Nai-Yi

    2010-10-01

    In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

  12. FUZZY CLUSTERING: APPLICATION ON ORGANIZATIONAL METAPHORS IN BRAZILIAN COMPANIES

    Directory of Open Access Journals (Sweden)

    Angel Cobo

    2012-08-01

    Full Text Available Different theories of organization and management are based on implicit images or metaphors. Nevertheless, a quantitative approach is needed to minimize human subjectivity or bias on metaphors studies. Hence, this paper analyzed the presence of metaphors and clustered them using fuzzy data mining techniques in a sample of 61 Brazilian companies that operate in the state of Rio Grande do Sul. For this purpose the results of a questionnaire answered by 198 employees of companies in the sample were analyzed by R free software. The results show that it is difficult to find a clear image in most organizations. In most cases characteristics of different images or metaphors are observed, so soft computing techniques are particularly appropriate for this type of analysis. However, according to these results, it is noted that the most present image in the organizations studied is that of “organisms” and the least present image is that of a “political system” and of an “instrument of domination”

  13. Adding-point strategy for reduced-order hypersonic aerothermodynamics modeling based on fuzzy clustering

    Science.gov (United States)

    Chen, Xin; Liu, Li; Zhou, Sida; Yue, Zhenjiang

    2016-09-01

    Reduced order models(ROMs) based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.

  14. Fuzzy C-means clustering for chromatographic fingerprints analysis: A gas chromatography-mass spectrometry case study.

    Science.gov (United States)

    Parastar, Hadi; Bazrafshan, Alisina

    2016-03-18

    Fuzzy C-means clustering (FCM) is proposed as a promising method for the clustering of chromatographic fingerprints of complex samples, such as essential oils. As an example, secondary metabolites of 14 citrus leaves samples are extracted and analyzed by gas chromatography-mass spectrometry (GC-MS). The obtained chromatographic fingerprints are divided to desired number of chromatographic regions. Owing to the fact that chromatographic problems, such as elution time shift and peak overlap can significantly affect the clustering results, therefore, each chromatographic region is analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to address these problems. Then, the resolved elution profiles are used to make a new data matrix based on peak areas of pure components to cluster by FCM. The FCM clustering parameters (i.e., fuzziness coefficient and number of cluster) are optimized by two different methods of partial least squares (PLS) as a conventional method and minimization of FCM objective function as our new idea. The results showed that minimization of FCM objective function is an easier and better way to optimize FCM clustering parameters. Then, the optimized FCM clustering algorithm is used to cluster samples and variables to figure out the similarities and dissimilarities among samples and to find discriminant secondary metabolites in each cluster (chemotype). Finally, the FCM clustering results are compared with those of principal component analysis (PCA), hierarchical cluster analysis (HCA) and Kohonon maps. The results confirmed the outperformance of FCM over the frequently used clustering algorithms. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. FUZZY METHOD FOR FAILURE CRITICALITY ANALYSIS

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The greatest benefit is realized from failure mode, effect and criticality analysis (FMECA) when it is done early in the design phase and tracks product changes as they evolve; design changes can then be made more economically than if the problems are discovered after the design has been completed. However, when the discovered design flaws must be prioritized for corrective actions, precise information on their probability of occurrence, the effect of the failure, and their detectability often are not availabe. To solve this problem, this paper described a new method, based on fuzzy sets, for prioritizing failures for corrective actions in a FMCEA. Its successful application to the container crane shows that the proposed method is both reasonable and practical.

  16. Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI

    Directory of Open Access Journals (Sweden)

    Nour-Eddine El Harchaoui

    2013-01-01

    Full Text Available The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM to initialize the parameters of possibilistic c-means (PCM, in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images.

  17. Face Recognition Method Based on Fuzzy 2DPCA

    Directory of Open Access Journals (Sweden)

    Xiaodong Li

    2014-01-01

    Full Text Available 2DPCA, which is one of the most important face recognition methods, is relatively sensitive to substantial variations in light direction, face pose, and facial expression. In order to improve the recognition performance of the traditional 2DPCA, a new 2DPCA algorithm based on the fuzzy theory is proposed in this paper, namely, the fuzzy 2DPCA (F2DPCA. In this method, applying fuzzy K-nearest neighbor (FKNN, the membership degree matrix of the training samples is calculated, which is used to get the fuzzy means of each class. The average of fuzzy means is then incorporated into the definition of the general scatter matrix with anticipation that it can improve classification result. The comprehensive experiments on the ORL, the YALE, and the FERET face database show that the proposed method can improve the classification rates and reduce the sensitivity to variations between face images caused by changes in illumination, face expression, and face pose.

  18. A fuzzy method for improving the functionality of search engines based on user's web interactions

    Directory of Open Access Journals (Sweden)

    Farzaneh Kabirbeyk

    2015-04-01

    Full Text Available Web mining has been widely used to discover knowledge from various sources in the web. One of the important tools in web mining is mining of web user’s behavior that is considered as a way to discover the potential knowledge of web user’s interaction. Nowadays, Website personalization is regarded as a popular phenomenon among web users and it plays an important role in facilitating user access and provides information of users’ requirements based on their own interests. Extracting important features about web user behavior plays a significant role in web usage mining. Such features are page visit frequency in each session, visit duration, and dates of visiting a certain pages. This paper presents a method to predict user’s interest and to propose a list of pages based on their interests by identifying user’s behavior based on fuzzy techniques called fuzzy clustering method. Due to the user’s different interests and use of one or more interest at a time, user’s interest may belong to several clusters and fuzzy clustering provide a possible overlap. Using the resulted cluster helps extract fuzzy rules. This helps detecting user’s movement pattern and using neural network a list of suggested pages to the users is provided.

  19. Comprehensive studies of hydrogeochemical processes and quality status of groundwater with tools of cluster, grouping analysis, and fuzzy set method using GIS platform: a case study of Dalcheon in Ulsan City, Korea.

    Science.gov (United States)

    Venkatramanan, S; Chung, S Y; Rajesh, R; Lee, S Y; Ramkumar, T; Prasanna, M V

    2015-08-01

    This research aimed at developing comprehensive assessments of physicochemical quality of groundwater for drinking and irrigation purposes at Dalcheon in Ulsan City, Korea. The mean concentration of major ions represented as follows: Ca (94.3 mg/L) > Mg (41.7 mg/L) > Na (19.2 mg/L) > K (3.2 mg/L) for cations and SO4 (351 mg/L) > HCO3 (169 mg/L) > Cl (19 mg/L) for anions. Thematic maps for physicochemical parameters of groundwater were prepared, classified, weighted, and integrated in GIS method with fuzzy logic. The maps exhibited that suitable zone of drinking and irrigation purpose occupied in SE, NE, and NW sectors. The undesirable zone of drinking purpose was observed in SW and central parts and that of irrigation was in the western part of the study area. This was influenced by improperly treated effluents from an abandoned iron ore mine, irrigation, and domestic fields. By grouping analysis, groundwater types were classified into Ca(HCO3)2, (Ca,Mg)Cl2, and CaCl2, and CaHCO3 was the most predominant type. Grouping analysis also showed three types of irrigation water such as C1S1, C1S2, and C1S3. C1S3 type of high salinity to low sodium hazard was the most dominant in the study area. Equilibrium processes elucidated the groundwater samples were in the saturated to undersaturated condition with respect to aragonite, calcite, dolomite, and gypsum due to precipitation and deposition processes. Cluster analysis suggested that high contents of SO4 and HCO3 with low Cl was related with water-rock interactions and along with mining impact. This study showed that the effluents discharged from mining waste was the main sources of groundwater quality deterioration.

  20. Simultaneous Forecast for Three Speciations of Heavy Metal Elements Using Fuzzy Cluster-Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    ZHAO Tian-qi; MENG Fan-yu; WANG Hong-yan; GAO Yan

    2012-01-01

    Abstract The three speciations(water extract,adsorption and organic speciations) of Cu,Zn,Fe and Mn in geo-chemical samples were determined by fuzzy cluster-artificial neural network(FC-ANN) method coupled with atomic absorption spectrometry.A back-propagation artificial neural network with one input node and three export nodes was constructed,which could forecaste three speciations of heavy metals simultaneously.In the learning sample set,the three speciations of each element were allowed to change in a wide concentration range and the accuracy of the analysis was apparently increased via the learning sample set optimized with the help of the fuzzy cluster analysis.The average relative errors of the three speciations of Cu,Zn,Fe or Mn from 100 geo-chemical samples were less than 5%.The relative standard deviations of the three speciations of each of four heavy metals were 0.008%-4.43%.

  1. New Results in Fuzzy Clustering Based on the Concept of Indistinguishability Relation

    Science.gov (United States)

    1984-01-01

    NEW RESULTS IN Fuzzy CLUSTERING BASED ON THE CONCEPT OF INDISTINGUISHABILITY RELATION KEYWORDS R . Lopez de Mantaras Facultat d ’Informatica...Universitat Politecnica de Barcelona Dulcet, 12. Barcelona-34. Spain. L. Valverde* Dept. de Matematiques i Estadistica Universitat Politecnica de... r -cluster that extend Ruspini’s definition (Ruspini, 1982). Our definition is based on the new concept of indis- tinguishability relation (Trillas

  2. Fuzzy Critical Path Method Based on Lexicographic Ordering

    Directory of Open Access Journals (Sweden)

    Phani Bushan Rao P

    2012-01-01

    Full Text Available The Critical Path Method (CPM is useful for planning and control of complex projects. The CPM identifies the critical activities in the critical path of an activity network. The successful implementation of CPM requires the availability of clear determined time duration for each activity. However, in practical situations this requirement is usually hard to fulfil since many of activities will be executed for the first time. Hence, there is always uncertainty about the time durations of activities in the network planning.  This has led to the development of fuzzy CPM.  In this paper, we use a Lexicographic ordering method for ranking fuzzy numbers to a critical path method in a fuzzy project network, where the duration time of each activity is represented by a trapezoidal fuzzy number. The proposed method is compared with fuzzy CPM based on different ranking methods of fuzzy numbers. The comparison reveals that the method proposed in this paper is more effective in determining the activity criticalities and finding the critical path.   This new method is simple in calculating fuzzy critical path than many methods proposed so far in literature.  

  3. Cluster head Election for CGSR Routing Protocol Using Fuzzy Logic Controller for Mobile Ad Hoc Network

    Directory of Open Access Journals (Sweden)

    K. Venkata Subbaiah

    2010-01-01

    Full Text Available The nodes in the mobile ad hoc networks act as router and host, the routing protocol is the primary issue and has to be supported before any applications can be deployed for mobile ad hoc networks. In recent many research protocols are proposed for finding an efficient route between the nodes. But most of the protocol’s that uses conventional techniques in routing; CBRP is a routing protocol that has a hierarchical-based design. This protocol divides the network area into several smaller areas called cluster. We propose a fuzzy logic based cluster head election using energy concept forcluster head routing protocol in MANET’S. Selecting an appropriate cluster head can save power for the whole mobile ad hoc network. Generally, Cluster Head election for mobile ad hoc network is based on the distance to the centroid of a cluster, and the closest one is elected as the cluster head'; or pick a node with the maximum battery capacity as the cluster head. In this paper, we present a cluster head election scheme using fuzzy logic system (FLS for mobile ad hoc networks. Three descriptors are used: distance of a node to the cluster centroid, its remaining battery capacity, and its degree of mobility. The linguistic knowledge of cluster head election based on these three descriptors is obtained from a group of network experts. 27 FLS rules are set up based on the linguistic knowledge. The output of the FLS provides a cluster head possibility, and node with the highest possibility is elected as the cluster head. The performance of fuzzy cluster head selection is evaluated using simulation, and is compared to LEACH protocol with out fuzzy cluster head election procedures and showed the proposed work is efficient than the previous one.

  4. Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering

    OpenAIRE

    Akara Sopharak; Sarah Barman; Bunyarit Uyyanonvara

    2009-01-01

    Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse se...

  5. Approximate Method for Solving the Linear Fuzzy Delay Differential Equations

    Directory of Open Access Journals (Sweden)

    S. Narayanamoorthy

    2015-01-01

    Full Text Available We propose an algorithm of the approximate method to solve linear fuzzy delay differential equations using Adomian decomposition method. The detailed algorithm of the approach is provided. The approximate solution is compared with the exact solution to confirm the validity and efficiency of the method to handle linear fuzzy delay differential equation. To show this proper features of this proposed method, numerical example is illustrated.

  6. A Modification on the Hesitant Fuzzy Set Lexicographical Ranking Method

    OpenAIRE

    Xiaodi Liu; Zengwen Wang; Shitao Zhang

    2016-01-01

    Recently, a novel hesitant fuzzy set (HFS) ranking technique based on the idea of lexicographical ordering is proposed and an example is presented to demonstrate that the proposed ranking method is invariant with multiple occurrences of any element of a hesitant fuzzy element (HFE). In this paper, we show by examples that the HFS lexicographical ordering method is sometimes invalid, and a modified ranking method is presented. In comparison with the HFS lexicographical ordering method, the mod...

  7. Fuzzy spectral clustering for automated delineation of chronic wound region using digital images.

    Science.gov (United States)

    Manohar Dhane, Dhiraj; Maity, Maitreya; Mungle, Tushar; Bar, Chittaranjan; Achar, Arun; Kolekar, Maheshkumar; Chakraborty, Chandan

    2017-04-23

    Chronic wound is an abnormal disease condition of localized injury to the skin and its underlying tissues having physiological impaired healing response. Assessment and management of such wound is a significant burden on the healthcare system. Currently, precise wound bed estimation depends on the clinical judgment and remains a difficult task. The paper introduces a novel method for ulcer boundary demarcation and estimation, using optical images captured by a hand-held digital camera. The proposed approach involves gray based fuzzy similarity measure using spatial knowledge of an image. The fuzzy measure is used to construct similarity matrix. The best color channel was chosen by calculating the mean contrast for 26 different color channels of 14 color spaces. It was found that Db color channel has highest mean contrast which provide best segmentation result in comparison with other color channels. The fuzzy spectral clustering (FSC) method was applied on Db color channel for effective delineation of wound region. The segmented wound regions were effectively post-processed using various morphological operations. The performance of proposed segmentation technique was validated by ground-truth images labeled by two experienced dermatologists and a surgeon. The FSC approach was tested on 70 images. FSC effectively segmented targeted ulcer boundary yielding 91.5% segmentation accuracy, 86.7%, Dice index and 79.0%. Jaccard score. The sensitivity and specificity was found to be 87.3% and 95.7% respectively. The performance evaluation shows the robustness of the proposed method of wound area segmentation and its potential to be used for designing patient comfort centric wound care system. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Combination of Neural Networks and Fuzzy Clustering Algorithm to Evalution Training Simulation-Based Training

    Directory of Open Access Journals (Sweden)

    Lida Pourjafar

    2016-07-01

    Full Text Available With the advancement of computer technology, computer simulation in the field of education are more realistic and more effective. The definition of simulation is to create a virtual environment that accurately and real experiences to improve the individual. So Simulation Based Training is the ability to improve, replace, create or manage a real experience and training in a virtual mode. Simulation Based Training also provides large amounts of information to learn, so use data mining techniques to process information in the case of education can be very useful. So here we used data mining to examine the impact of simulation-based training. The database created in cooperation with relevant institutions, including 17 features. To study the effect of selected features, LDA method and Pearson's correlation coefficient was used along with genetic algorithm. Then we use fuzzy clustering to produce fuzzy system and improved it using Neural Networks. The results showed that the proposed method with reduced dimensions have 3% better than other methods.

  9. An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques

    Directory of Open Access Journals (Sweden)

    Elid Rubio

    2017-01-01

    Full Text Available In this work an extension of the Fuzzy Possibilistic C-Means (FPCM algorithm using Type-2 Fuzzy Logic Techniques is presented, and this is done in order to improve the efficiency of FPCM algorithm. With the purpose of observing the performance of the proposal against the Interval Type-2 Fuzzy C-Means algorithm, several experiments were made using both algorithms with well-known datasets, such as Wine, WDBC, Iris Flower, Ionosphere, Abalone, and Cover type. In addition some experiments were performed using another set of test images to observe the behavior of both of the above-mentioned algorithms in image preprocessing. Some comparisons are performed between the proposed algorithm and the Interval Type-2 Fuzzy C-Means (IT2FCM algorithm to observe if the proposed approach has better performance than this algorithm.

  10. Adaptive fuzzy leader clustering of complex data sets in pattern recognition

    Science.gov (United States)

    Newton, Scott C.; Pemmaraju, Surya; Mitra, Sunanda

    1992-01-01

    A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.

  11. Designing fuzzy inference system based on improved gradient descent method

    Institute of Scientific and Technical Information of China (English)

    Zhang Liquan; Shao Cheng

    2006-01-01

    The distribution of sampling data influences completeness of rule base so that extrapolating missing rules is very difficult. Based on data mining, a self-learning method is developed for identifying fuzzy model and extrapolating missing rules, by means of confidence measure and the improved gradient descent method. The proposed approach can not only identify fuzzy model, update its parameters and determine optimal output fuzzy sets simultaneously, but also resolve the uncontrollable problem led by the regions that data do not cover. The simulation results show the effectiveness and accuracy of the proposed approach with the classical truck backer-upper control problem verifying.

  12. Marginal linearization method in modeling on fuzzy control systems

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    Marginal linearization method in modeling on fuzzy control systems is proposed, which is to deal with the nonlinear model with variable coefficients. The method can turn a nonlinear model with variable coefficients into a linear model with variable coefficients in the way that the membership functions of the fuzzy sets in fuzzy partitions of the universes are changed from triangle waves into rectangle waves. However, the linearization models are incomplete in their forms because of their lacking some items. For solving this problem, joint approximation by using linear models is introduced. The simulation results show that marginal linearization models are of higher approximation precision than their original nonlinear models.

  13. A Robust Background Removal Algortihms Using Fuzzy C-Means Clustering

    Directory of Open Access Journals (Sweden)

    S.Lakshmi

    2013-04-01

    Full Text Available Background subtraction is typically one of the first steps carried out in motion detection using static video cameras. This paper presents a novel method for background removal that processes only some pixels of each image. Some regions of interest of the objects in the image or frame are located with the help of edgedetector. Once the region is detected only that area will be segmented instead of processing the whole image. This method achieves a significant reduction in computation time that can be used forsubsequent image analysis. In this paper we detect the foreground object with the help of edge detector and combinethe Fuzzy c-means clustering algorithm to segment the object by means of subtracting the current frame from the previous frame, the accuratebackground is identified.

  14. Estimation of Water Quality Parameters Using the Regression Model with Fuzzy K-Means Clustering

    Directory of Open Access Journals (Sweden)

    Muntadher A. SHAREEF

    2014-07-01

    Full Text Available the traditional methods in remote sensing used for monitoring and estimating pollutants are generally relied on the spectral response or scattering reflected from water. In this work, a new method has been proposed to find contaminants and determine the Water Quality Parameters (WQPs based on theories of the texture analysis. Empirical statistical models have been developed to estimate and classify contaminants in the water. Gray Level Co-occurrence Matrix (GLCM is used to estimate six texture parameters: contrast, correlation, energy, homogeneity, entropy and variance. These parameters are used to estimate the regression model with three WQPs. Finally, the fuzzy K-means clustering was used to generalize the water quality estimation on all segmented image. Using the in situ measurements and IKONOS data, the obtained results show that texture parameters and high resolution remote sensing able to monitor and predicate the distribution of WQPs in large rivers.

  15. A Soft Discretization Method of Celestial Spectrum Characteristic Line Based on Fuzzy C-Means Clustering%基于模糊C均值聚类的天文光谱特征线软离散化

    Institute of Scientific and Technical Information of China (English)

    张继福; 李鑫; 杨海峰

    2012-01-01

    连续数值属性离散化是天文光谱数据预处理中的主要研究内容之一.针对天文光谱特征线,提出了一种基于改进模糊C均值聚类的天文光谱特征线软离散化算法.该算法首先利用样本的密度值选取特征线的候选初始模糊聚类中心,有效地克服了对噪声数据敏感的缺陷;其次采用决策表中的相容性作为评判标准,动态的调节聚类参数,以达到优化的光谱特征线离散化效果;最后采用晚型星、类星体、高红移类星体SDSS天文光谱特征线数据集.实验验证了该算法具有较高的识别率,为天文光谱特征线数据预处理提供了一种新途径.%Discretization of continuous numerical attribute is one of the important research works in the preprocessing of celestial spectrum data. For characteristic line of celestial spectrum, a soft discretization algorithm is presented by using improved fuzzy C-means clustering. Firstly, candidate fuzzy clustering centers of characteristic line are chosen by using density values of sample data, so that its anti-noise ability is improved. Secondly, parameters in the fuzzy clustering are dynamically adjusted by taking compatibility of decision table as criteria, so that optimal discretization effect of the characteristic line is achieved. In the end, experimental results effectively validate that the algorithm has higher correct recognition rate of the algorithm by using three SDSS celestial spectrum data sets of high-redshift quasars, late-type star and quasars.

  16. Evaluating water management strategies in watersheds by new hybrid Fuzzy Analytical Network Process (FANP) methods

    Science.gov (United States)

    RazaviToosi, S. L.; Samani, J. M. V.

    2016-03-01

    Watersheds are considered as hydrological units. Their other important aspects such as economic, social and environmental functions play crucial roles in sustainable development. The objective of this work is to develop methodologies to prioritize watersheds by considering different development strategies in environmental, social and economic sectors. This ranking could play a significant role in management to assign the most critical watersheds where by employing water management strategies, best condition changes are expected to be accomplished. Due to complex relations among different criteria, two new hybrid fuzzy ANP (Analytical Network Process) algorithms, fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and fuzzy max-min set methods are used to provide more flexible and accurate decision model. Five watersheds in Iran named Oroomeyeh, Atrak, Sefidrood, Namak and Zayandehrood are considered as alternatives. Based on long term development goals, 38 water management strategies are defined as subcriteria in 10 clusters. The main advantage of the proposed methods is its ability to overcome uncertainty. This task is accomplished by using fuzzy numbers in all steps of the algorithms. To validate the proposed method, the final results were compared with those obtained from the ANP algorithm and the Spearman rank correlation coefficient is applied to find the similarity in the different ranking methods. Finally, the sensitivity analysis was conducted to investigate the influence of cluster weights on the final ranking.

  17. Edge detection methods based on generalized type-2 fuzzy logic

    CERN Document Server

    Gonzalez, Claudia I; Castro, Juan R; Castillo, Oscar

    2017-01-01

    In this book four new methods are proposed. In the first method the generalized type-2 fuzzy logic is combined with the morphological gra-dient technique. The second method combines the general type-2 fuzzy systems (GT2 FSs) and the Sobel operator; in the third approach the me-thodology based on Sobel operator and GT2 FSs is improved to be applied on color images. In the fourth approach, we proposed a novel edge detec-tion method where, a digital image is converted a generalized type-2 fuzzy image. In this book it is also included a comparative study of type-1, inter-val type-2 and generalized type-2 fuzzy systems as tools to enhance edge detection in digital images when used in conjunction with the morphologi-cal gradient and the Sobel operator. The proposed generalized type-2 fuzzy edge detection methods were tested with benchmark images and synthetic images, in a grayscale and color format. Another contribution in this book is that the generalized type-2 fuzzy edge detector method is applied in the preproc...

  18. Challenges And Results of the Applications of Fuzzy Logic in the Classification of Rich Galaxy Clusters

    Science.gov (United States)

    Girola Schneider, R.

    2017-07-01

    The fuzzy logic is a branch of the artificial intelligence founded on the concept that everything is a matter of degree. It intends to create mathematical approximations on the resolution of certain types of problems. In addition, it aims to produce exact results obtained from imprecise data, for which it is particularly useful for electronic and computer applications. This enables it to handle vague or unspecific information when certain parts of a system are unknown or ambiguous and, therefore, they cannot be measured in a reliable manner. Also, when the variation of a variable can produce an alteration on the others The main focus of this paper is to prove the importance of these techniques formulated from a theoretical analysis on its application on ambiguous situations in the field of the rich clusters of galaxies. The purpose is to show its applicability in the several classification systems proposed for the rich clusters, which are based on criteria such as the level of richness of the cluster, the distribution of the brightest galaxies, whether there are signs of type-cD galaxies or not or the existence of sub-clusters. Fuzzy logic enables the researcher to work with "imprecise" information implementing fuzzy sets and combining rules to define actions. The control systems based on fuzzy logic join input variables that are defined in terms of fuzzy sets through rule groups that produce one or several output values of the system under study. From this context, the application of the fuzzy logic's techniques approximates the solution of the mathematical models in abstractions about the rich galaxy cluster classification of physical properties in order to solve the obscurities that must be confronted by an investigation group in order to make a decision.

  19. Challenges And Results of the Applications of Fuzzy Logic in the Classification of Rich Galaxy Clusters

    Science.gov (United States)

    Santiago Girola Schneider, Rafael

    2015-08-01

    The fuzzy logic is a branch of the artificial intelligence founded on the concept that 'everything is a matter of degree.' It intends to create mathematical approximations on the resolution of certain types of problems. In addition, it aims to produce exact results obtained from imprecise data, for which it is particularly useful for electronic and computer applications. This enables it to handle vague or unspecific information when certain parts of a system are unknown or ambiguous and, therefore, they cannot be measured in a reliable manner. Also, when the variation of a variable can produce an alteration on the others.The main focus of this paper is to prove the importance of these techniques formulated from a theoretical analysis on its application on ambiguous situations in the field of the rich clusters of galaxies. The purpose is to show its applicability in the several classification systems proposed for the rich clusters, which are based on criteria such as the level of richness of the cluster, the distribution of the brightest galaxies, whether there are signs of type-cD galaxies or not or the existence of sub-clusters.Fuzzy logic enables the researcher to work with “imprecise” information implementing fuzzy sets and combining rules to define actions. The control systems based on fuzzy logic join input variables that are defined in terms of fuzzy sets through rule groups that produce one or several output values of the system under study. From this context, the application of the fuzzy logic’s techniques approximates the solution of the mathematical models in abstractions about the rich galaxy cluster classification of physical properties in order to solve the obscurities that must be confronted by an investigation group in order to make a decision.

  20. Fuzzy-hybrid land vehicle driveline modelling based on a moving window subtractive clustering approach

    Science.gov (United States)

    Economou, J. T.; Knowles, K.; Tsourdos, A.; White, B. A.

    2011-02-01

    In this article, the fuzzy-hybrid modelling (FHM) approach is used and compared to the input-output system Takagi-Sugeno (TS) modelling approach which correlates the drivetrain power flow equations with the vehicle dynamics. The output power relations were related to the drivetrain bounded efficiencies and also to the wheel slips. The model relates also to the wheel and ground interactions via suitable friction coefficient models relative to the wheel slip profiles. The wheel slip had a significant efficiency contribution to the overall driveline system efficiency. The peak friction slip and peak coefficient of friction values are known a priori during the analysis. Lastly, the rigid body dynamical power has been verified through both simulation and experimental results. The mathematical analysis has been supported throughout the paper via experimental data for a specific electric robotic vehicle. The identification of the localised and input-output TS models for the fuzzy hybrid and the experimental data were obtained utilising the subtractive clustering (SC) methodology. These results were also compared to a real-time TS SC approach operating on periodic time windows. This article concludes with the benefits of the real-time FHM method for the vehicle electric driveline due to the advantage of both the analytical TS sub-model and the physical system modelling for the remaining process which can be clearly utilised for control purposes.

  1. A Generalized Automatic Hybrid Fuzzy-Based GA-PSO Clustering Approach

    Directory of Open Access Journals (Sweden)

    Amir Hooshang Mazinan, ,

    2014-09-01

    Full Text Available The main contribution of the present research arises from developing the traditional methods in the area of segmentation of brain magnetic resonance imaging (MRI. Contemporary research is now developing techniques to solve the whole considerable problems in this field, such as the fuzzy local information c-mean (FLICM approach that incorporate the local spatial and the gray level information. It should be noted that the present approach is robust against noise, although the high computational complexity is not truly ignored. A novel approach in segmentation of brain MRI has been investigated and presented through the proposed research. Because of so many noises embedded in the acquiring procedure, like eddy currents, the segmentation of the brain MR is now tangibly taken into account as a difficult task. Fuzzy-based clustering algorithm is one of the solutions in the same way. But, it is so sensitive to change through noise and other imaging artifacts. The idea of combining the genetic algorithm (GA and particle swarm optimization (PSO for the purpose of generalizing the FLICM is the ultimate goal in the present investigation, since the computational complexity could actually be reduced. The experiments with a number of simulated images as well as the clinical MRI data illustrate that the proposed approach is applicable and effective.

  2. Fuzzy clustering of EEE components for space industry

    Science.gov (United States)

    Orlov, V. I.; Stashkov, D. V.; Kazakovtsev, L. A.; Stupina, A. A.

    2016-11-01

    One of the most important problems of the space industry is obtaining reliable methods of automatic grouping (clustering) of specialized EEE components for using in space systems. The main purpose of automatic grouping of EEE components on a set different parameters is the most legible splitting group of EEE components into several homogeneous production batches produced from a single bath of raw materials. The Expectation Maximization algorithm first time applied for the classification of EEE components.

  3. Fuzzy multiple objective decision making methods and applications

    CERN Document Server

    Lai, Young-Jou

    1994-01-01

    In the last 25 years, the fuzzy set theory has been applied in many disciplines such as operations research, management science, control theory, artificial intelligence/expert system, etc. In this volume, methods and applications of crisp, fuzzy and possibilistic multiple objective decision making are first systematically and thoroughly reviewed and classified. This state-of-the-art survey provides readers with a capsule look into the existing methods, and their characteristics and applicability to analysis of fuzzy and possibilistic programming problems. To realize practical fuzzy modelling, it presents solutions for real-world problems including production/manufacturing, location, logistics, environment management, banking/finance, personnel, marketing, accounting, agriculture economics and data analysis. This book is a guided tour through the literature in the rapidly growing fields of operations research and decision making and includes the most up-to-date bibliographical listing of literature on the topi...

  4. Segmentation and Labelling of Human Spine MR Images Using Fuzzy Clustering

    Directory of Open Access Journals (Sweden)

    Jiyo.S.Athertya

    2016-04-01

    Full Text Available Computerized medical image segmentation is a challe nging area because of poor resolution and weak contrast. The predominantly used conventio nal clustering techniques and the thresholding methods suffer from limitations owing to their heavy dependence on user interactions. Uncertainties prevalent in an image c annot be captured by these techniques. The performance further deteriorates when the images ar e corrupted by noise, outliers and other artifacts. The objective of this paper is to develo p an effective robust fuzzy C- means clustering for segmenting vertebral body from magnetic resonan ce images. The motivation for this work is that spine appearance, shape and geometry measureme nts are necessary for abnormality detection and thus proper localisation and labellin g will enhance the diagnostic output of a physician. The method is compared with Otsu thresho lding and K-means clustering to illustrate the robustness. The reference standard for validation was the annot ated images from the radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the segmentation.

  5. A goal programming method for deriving fuzzy priorities of criteria from inconsistent fuzzy comparison matrices

    Directory of Open Access Journals (Sweden)

    Mohammad Izadikhah

    2012-01-01

    Full Text Available Decision making problem is the process of finding the best option from all of the feasible alternatives. One of the most important concepts in decision making process is to identify the weights of criteria. In real-world situation, because of incomplete or non-obtainable information, the data (attributes are often not deterministic and can be treated in forms of fuzzy numbers. This paper investigates a method for deriving the weights of criteria from the pair-wise comparison matrix with fuzzy elements. Finding the weights of criteria has been one of the most important issues in the field of decision-making and the present method uses goal programming to solve the resulted model. In addition, using a ranking function we convert each obtained fuzzy weight to a crisp one, which makes it possible to compare the criteria. The proposed model of this paper is supported by several examples and a case study.

  6. Measuring efficiency of a hierarchical organization with fuzzy DEA method

    OpenAIRE

    LUBAN Florica

    2009-01-01

    The paper analyses how the data envelopment analysis (DEA) and fuzzy set theory can be used to measure and evaluate the efficiency of a hierarchical system with n decision making units and a coordinating unit. It is presented a model for determining the of activity levels of decision making units so as to achieve both fuzzy objectives of achieving global target levels of coordination unit on the inputs and outputs and individual target levels of decision making units, and then some methods to...

  7. Some Fuzzy Logic Based Methods to Deal with Sensorial Information

    Institute of Scientific and Technical Information of China (English)

    Bernadette Bouchon-Meunier

    2004-01-01

    Sensorial information is very difficult to elicit, to represent and to manage because of its complexity. Fuzzy logic provides an interesting means to deal with such information, since it allows us to represent imprecise, vague or incomplete descriptions, which are very common in the management of subjective information. Aggregation methods proposed by fuzzy logic are further useful to combine the characteristics of the various components of sensorial information.

  8. Fuzzy C-Means Clustering Based Phonetic Tied-Mixture HMM in Speech Recognition

    Institute of Scientific and Technical Information of China (English)

    XU Xiang-hua; ZHU Jie; GUO Qiang

    2005-01-01

    A fuzzy clustering analysis based phonetic tied-mixture HMM(FPTM) was presented to decrease parameter size and improve robustness of parameter training. FPTM was synthesized from state-tied HMMs by a modified fuzzy C-means clustering algorithm. Each Gaussian codebook of FPTM was built from Gaussian components within the same root node in phonetic decision tree. The experimental results on large vocabulary Mandarin speech recognition show that compared with conventional phonetic tied-mixture HMM and state-tied HMM with approximately the same number of Gaussian mixtures, FPTM achieves word error rate reductions by 4.84% and 13.02 % respectively. Combining the two schemes of mixing weights pruning and Gaussian centers fuzzy merging, a significantly parameter size reduction was achieved with little impact on recognition accuracy.

  9. Measuring the performance of FCM versus PSO for fuzzy clustering problems

    Directory of Open Access Journals (Sweden)

    Amir Reza Soltani

    2013-06-01

    Full Text Available Clustering cellular manufacturing plays an important role in many industrial engineering problems. This paper investigates the performance of two methods of heuristic and metaheuristics fuzzy clustering. The proposed method investigates heuristic well-known FCM and particle swarm optimization (PSO on some well-known benchmarks. We use two criteria of J(P as well as Xie-Beni to compare the results. Three parameters of PSO method is tuned using design of experiment and then the results of PSO are compared versus FCM method in terms of two mentioned criteria. The proposed models are run for each instance 10 different times and, using ANOVA test, the means of two methods are compared. While the results of ANOVA do not indicate any meaningful difference between PSO and FCM in terms of J(P, we have found some meaningful differences between PSO and FCM in terms of Xie-Beni criterion. In other words, PSO performs better than FCM in terms of Xie-Beni.

  10. Signal trend identification with fuzzy methods.

    Energy Technology Data Exchange (ETDEWEB)

    Reifman, J.; Tsoukalas, L. H.; Wang, X.; Wei, T. Y. C.

    1999-08-19

    A fuzzy-logic-based methodology for on-line signal trend identification is introduced. Although signal trend identification is complicated by the presence of noise, fuzzy logic can help capture important features of on-line signals and classify incoming power plant signals into increasing, decreasing and steady-state trend categories. In order to verify the methodology, a code named PROTREN is developed and tested using plant data. The results indicate that the code is capable of detecting transients accurately, identifying trends reliably, and not misinterpreting a steady-state signal as a transient one.

  11. Disorder Speech Clustering For Clinical Data Using Fuzzy C-Means Clustering And Comparison With SVM Classification

    Directory of Open Access Journals (Sweden)

    C.R.Bharathi

    2012-11-01

    Full Text Available Speech is the most vital skill of communication. Stammering is speech which is hesitant, stumbling, tense or jerky to the extent that it causes anxiety to the speaker. In the existing system, there are many effective treatments for the problem of stammering. Speech-language therapy is the treatment for most kids with speech and/or language disorders. In this work, mild level of mental retardation (MR children speech samples were taken for consideration. The proposed work is, the acute spot must be identified for affording speech training to the speech disordered children. To begin with the proposed work, initially Clustering of speech is done using Fuzzy C-means Clustering Algorithm. Feature Extraction is implemented using Mel Frequency Cepstrum Coefficients (MFCC and dimensionality reduction of features extracted is implemented using Principal Component Analysis (PCA. Finally the features were clustered using Fuzzy C-Means algorithm and compared with SVM classifier output[13].

  12. Identification of certain cancer-mediating genes using Gaussian fuzzy cluster validity index

    Indian Academy of Sciences (India)

    Anupam Ghosh; Rajat K De

    2015-10-01

    In this article, we have used an index, called Gaussian fuzzy index (GFI), recently developed by the authors, based on the notion of fuzzy set theory, for validating the clusters obtained by a clustering algorithm applied on cancer gene expression data. GFI is then used for the identification of genes that have altered quite significantly from normal state to carcinogenic state with respect to their mRNA expression patterns. The effectiveness of the methodology has been demonstrated on three gene expression cancer datasets dealing with human lung, colon and leukemia. The performance of GFI is compared with 19 exiting cluster validity indices. The results are appropriately validated biologically and statistically. In this context, we have used biochemical pathways, -value statistics of GO attributes, -test and -score for the validation of the results. It has been reported that GFI is capable of identifying high-quality enriched clusters of genes, and thereby is able to select more cancer-mediating genes.

  13. Intuitionistic Trapezoidal Fuzzy Group Decision-Making Based on Prospect Choquet Integral Operator and Grey Projection Pursuit Dynamic Cluster

    Directory of Open Access Journals (Sweden)

    Jiahang Yuan

    2017-01-01

    Full Text Available In consideration of the interaction among attributes and the influence of decision makers’ risk attitude, this paper proposes an intuitionistic trapezoidal fuzzy aggregation operator based on Choquet integral and prospect theory. With respect to a multiattribute group decision-making problem, the prospect value functions of intuitionistic trapezoidal fuzzy numbers are aggregated by the proposed operator; then a grey relation-projection pursuit dynamic cluster method is developed to obtain the ranking of alternatives; the firefly algorithm is used to optimize the objective function of projection for obtaining the best projection direction of grey correlation projection values, and the grey correlation projection values are evaluated, which are applied to classify, rank, and prefer the alternatives. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible.

  14. The upper bound of the optimal number of clusters in fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The upper bound of the optimal number of clusters in clustering algorithm is studied in this paper. A new method is proposed to solve this issue. This method shows that the rule cmax≤n, which is popular in current papers, is reasonable in some sense. The above conclusion is tested and analyzed by some typical examples in the literature, which demonstrates the validity of the new method.

  15. Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model

    Science.gov (United States)

    Li, X. L.; Zhao, Q. H.; Li, Y.

    2017-09-01

    Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

  16. A Modification on the Hesitant Fuzzy Set Lexicographical Ranking Method

    Directory of Open Access Journals (Sweden)

    Xiaodi Liu

    2016-12-01

    Full Text Available Recently, a novel hesitant fuzzy set (HFS ranking technique based on the idea of lexicographical ordering is proposed and an example is presented to demonstrate that the proposed ranking method is invariant with multiple occurrences of any element of a hesitant fuzzy element (HFE. In this paper, we show by examples that the HFS lexicographical ordering method is sometimes invalid, and a modified ranking method is presented. In comparison with the HFS lexicographical ordering method, the modified ranking method is more reasonable in more general cases.

  17. Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System.

    Directory of Open Access Journals (Sweden)

    Vessela Krasteva

    Full Text Available This study presents a 2-stage heartbeat classifier of supraventricular (SVB and ventricular (VB beats. Stage 1 makes computationally-efficient classification of SVB-beats, using simple correlation threshold criterion for finding close match with a predominant normal (reference beat template. The non-matched beats are next subjected to measurement of 20 basic features, tracking the beat and reference template morphology and RR-variability for subsequent refined classification in SVB or VB-class by Stage 2. Four linear classifiers are compared: cluster, fuzzy, linear discriminant analysis (LDA and classification tree (CT, all subjected to iterative training for selection of the optimal feature space among extended 210-sized set, embodying interactive second-order effects between 20 independent features. The optimization process minimizes at equal weight the false positives in SVB-class and false negatives in VB-class. The training with European ST-T, AHA, MIT-BIH Supraventricular Arrhythmia databases found the best performance settings of all classification models: Cluster (30 features, Fuzzy (72 features, LDA (142 coefficients, CT (221 decision nodes with top-3 best scored features: normalized current RR-interval, higher/lower frequency content ratio, beat-to-template correlation. Unbiased test-validation with MIT-BIH Arrhythmia database rates the classifiers in descending order of their specificity for SVB-class: CT (99.9%, LDA (99.6%, Cluster (99.5%, Fuzzy (99.4%; sensitivity for ventricular ectopic beats as part from VB-class (commonly reported in published beat-classification studies: CT (96.7%, Fuzzy (94.4%, LDA (94.2%, Cluster (92.4%; positive predictivity: CT (99.2%, Cluster (93.6%, LDA (93.0%, Fuzzy (92.4%. CT has superior accuracy by 0.3-6.8% points, with the advantage for easy model complexity configuration by pruning the tree consisted of easy interpretable 'if-then' rules.

  18. SAR Ice Classification Using Fuzzy Screening Method

    Science.gov (United States)

    Gill, R. S.

    2003-04-01

    A semi-automatic SAR sea ice classification algorithm is described. It is based on combining the information in the original SAR data with those in the three 'image' products derived from it, namely Power-to-Mean Ratio (PMR), the Gamma distribution and the second order texture parameter entropy, respectively. The latter products contain information which is often useful during the manual interpretation of the images. The technique used to fuse the information in these products is based on a method c lled Multi Experts Multi Criteria Decision Making fuzzy a screening. The Multiple Experts in this case are the above four 'image' products. The two criteria used currently for making decisions are the Kolmogorov-Smirnov distribution matching and the statistical mean of different surface classes. The algorithm classifies an image into any number of predefined classes of sea ice and open water. The representative classes of these surface types are manually identified by the user. Further, as SAR signals from sea ice covered regions and open water are ambiguous, it was found that a minimum of 4 pre-identified surface classes (calm and turbulent water and sea ice with low and high backscatter values) are required to accurately classify an image. Best results are obtained when a total of 8 surface classes (2 each of sea ice and open water in the near range and a similar number in the far range of the SAR image) are used. The main advantage of using this image classification scheme is that, like neural networks, no prior knowledge is required of the statistical distribution of the different surface types. Furthermore, unlike the methods based on neural networks, no prior data sets are required to train the algorithm. All the information needed for image classification by the method is contained in the individual SAR images and associated products. Initial results illustrating the potential of this ice classification algorithm using the RADARSAT ScanSAR Wide data are presented

  19. Application of Fuzzy C-Means Clustering Algorithm Based on Particle Swarm Optimization in Computer Forensics

    Science.gov (United States)

    Wang, Deguang; Han, Baochang; Huang, Ming

    Computer forensics is the technology of applying computer technology to access, investigate and analysis the evidence of computer crime. It mainly include the process of determine and obtain digital evidence, analyze and take data, file and submit result. And the data analysis is the key link of computer forensics. As the complexity of real data and the characteristics of fuzzy, evidence analysis has been difficult to obtain the desired results. This paper applies fuzzy c-means clustering algorithm based on particle swarm optimization (FCMP) in computer forensics, and it can be more satisfactory results.

  20. Fuzzy Based Energy Efficient Multiple Cluster Head Selection Routing Protocol for Wireless Sensor Networks

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    Sohel Rana

    2015-03-01

    Full Text Available The Wireless Sensor Network (WSN is made up with small batteries powered sensor devices with lim-ited energy resources within it. These sensor nodes are used to monitor physical or environmental conditions and to pass their data through the wireless network to the main location. One of the crucial issues in wireless sensor network is to create a more energy efficient system. Clustering is one kind of mechanism in Wireless Sensor Networks to prolong the network lifetime and to reduce network energy consumption. In this paper, we propose a new routing protocol called Fuzzy Based Energy Effi-cient Multiple Cluster Head Selection Routing Protocol (FEMCHRP for Wireless Sensor Network. The routing process involves the Clustering of nodes and the selection of Cluster Head (CH nodes of these clusters which sends all the information to the Cluster Head Leader (CHL. After that, the cluster head leaders send aggregated data to the Base Station (BS. The selection of cluster heads and cluster head leaders is performed by using fuzzy logic and the data transmission process is performed by shortest energy path which is selected applying Dijkstra Algorithm. The simulation results of this research are compared with other protocols BCDCP, CELRP and ECHERP to evaluate the performance of the proposed routing protocol. The evaluation concludes that the proposed routing protocol is better in prolonging network lifetime and balancing energy consumption.

  1. Multicriteria Decision Making Method Based on the Higher Order Hesitant Fuzzy Soft Set.

    Science.gov (United States)

    Farhadinia, B

    2014-01-01

    The main goal of this contribution is to introduce the concept of higher order hesitant fuzzy soft set as an extension of fuzzy soft set that encompasses most of the existing extensions of fuzzy soft set as special cases. Furthermore, this new concept provides us with a method for dealing with multicriteria fuzzy decision making problems which are difficult to explain in other existing extensions of fuzzy soft set theory, especially when problems involve parameters with different-dimensional levels.

  2. Evaluation of E-Learners Behaviour using Different Fuzzy Clustering Models: A Comparative Study

    CERN Document Server

    Hogo, Mofreh A

    2010-01-01

    This paper introduces an evaluation methodologies for the e-learners' behaviour that will be a feedback to the decision makers in e-learning system. Learner's profile plays a crucial role in the evaluation process to improve the e-learning process performance. The work focuses on the clustering of the e-learners based on their behaviour into specific categories that represent the learner's profiles. The learners' classes named as regular, workers, casual, bad, and absent. The work may answer the question of how to return bad students to be regular ones. The work presented the use of different fuzzy clustering techniques as fuzzy c-means and kernelized fuzzy c-means to find the learners' categories and predict their profiles. The paper presents the main phases as data description, preparation, features selection, and the experiments design using different fuzzy clustering models. Analysis of the obtained results and comparison with the real world behavior of those learners proved that there is a match with per...

  3. Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering.

    Science.gov (United States)

    Gong, Maoguo; Zhou, Zhiqiang; Ma, Jingjing

    2012-04-01

    This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.

  4. STATISTICS OF FUZZY DATA

    Directory of Open Access Journals (Sweden)

    Orlov A. I.

    2016-05-01

    Full Text Available Fuzzy sets are the special form of objects of nonnumeric nature. Therefore, in the processing of the sample, the elements of which are fuzzy sets, a variety of methods for the analysis of statistical data of any nature can be used - the calculation of the average, non-parametric density estimators, construction of diagnostic rules, etc. We have told about the development of our work on the theory of fuzziness (1975 - 2015. In the first of our work on fuzzy sets (1975, the theory of random sets is regarded as a generalization of the theory of fuzzy sets. In non-fiction series "Mathematics. Cybernetics" (publishing house "Knowledge" in 1980 the first book by a Soviet author fuzzy sets is published - our brochure "Optimization problems and fuzzy variables". This book is essentially a "squeeze" our research of 70-ies, ie, the research on the theory of stability and in particular on the statistics of objects of non-numeric nature, with a bias in the methodology. The book includes the main results of the fuzzy theory and its note to the random set theory, as well as new results (first publication! of statistics of fuzzy sets. On the basis of further experience, you can expect that the theory of fuzzy sets will be more actively applied in organizational and economic modeling of industry management processes. We discuss the concept of the average value of a fuzzy set. We have considered a number of statements of problems of testing statistical hypotheses on fuzzy sets. We have also proposed and justified some algorithms for restore relationships between fuzzy variables; we have given the representation of various variants of fuzzy cluster analysis of data and variables and described some methods of collection and description of fuzzy data

  5. Nonlinear system identification by Gustafson-Kessel fuzzy clustering and supervised local model network learning for the drug absorption spectra process.

    Science.gov (United States)

    Teslic, Luka; Hartmann, Benjamin; Nelles, Oliver; Skrjanc, Igor

    2011-12-01

    This paper deals with the problem of fuzzy nonlinear model identification in the framework of a local model network (LMN). A new iterative identification approach is proposed, where supervised and unsupervised learning are combined to optimize the structure of the LMN. For the purpose of fitting the cluster-centers to the process nonlinearity, the Gustafsson-Kessel (GK) fuzzy clustering, i.e., unsupervised learning, is applied. In combination with the LMN learning procedure, a new incremental method to define the number and the initial locations of the cluster centers for the GK clustering algorithm is proposed. Each data cluster corresponds to a local region of the process and is modeled with a local linear model. Since the validity functions are calculated from the fuzzy covariance matrices of the clusters, they are highly adaptable and thus the process can be described with a very sparse amount of local models, i.e., with a parsimonious LMN model. The proposed method for constructing the LMN is finally tested on a drug absorption spectral process and compared to two other methods, namely, Lolimot and Hilomot. The comparison between the experimental results when using each method shows the usefulness of the proposed identification algorithm.

  6. CHANGE DETECTION BY FUSING ADVANTAGES OF THRESHOLD AND CLUSTERING METHODS

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    M. Tan

    2017-09-01

    Full Text Available In change detection (CD of medium-resolution remote sensing images, the threshold and clustering methods are two kinds of the most popular ones. It is found that the threshold method of the expectation maximum (EM algorithm usually generates a CD map including many false alarms but almost detecting all changes, and the fuzzy local information c-means algorithm (FLICM obtains a homogeneous CD map but with some missed detections. Therefore, we aim to design a framework to improve CD results by fusing the advantages of threshold and clustering methods. Experimental results indicate the effectiveness of the proposed method.

  7. Soil-landscape modelling using fuzzy c-means clustering of attribute data derived from a Digital Elevation Model (DEM).

    NARCIS (Netherlands)

    Bruin, de S.; Stein, A.

    1998-01-01

    This study explores the use of fuzzy c-means clustering of attribute data derived from a digital elevation model to represent transition zones in the soil-landscape. The conventional geographic model used for soil-landscape description is not able to properly deal with these. Fuzzy c-means clusterin

  8. Improved FIFO Scheduling Algorithm Based on Fuzzy Clustering in Cloud Computing

    Directory of Open Access Journals (Sweden)

    Jian Li

    2017-02-01

    Full Text Available In cloud computing, some large tasks may occupy too many resources and some small tasks may wait for a long time based on First-In-First-Out (FIFO scheduling algorithm. To reduce tasks’ waiting time, we propose a task scheduling algorithm based on fuzzy clustering algorithms. We construct a task model, resource model, and analyze tasks’ preference, then classify resources with fuzzy clustering algorithms. Based on the parameters of cloud tasks, the algorithm will calculate resource expectation and assign tasks to different resource clusters, so the complexity of resource selection will be decreased. As a result, the algorithm will reduce tasks’ waiting time and improve the resource utilization. The experiment results show that the proposed algorithm shortens the execution time of tasks and increases the resource utilization.

  9. Fuzzy Logic in Inverse Continuous Method

    Directory of Open Access Journals (Sweden)

    Víťazoslav Krúpa

    2004-12-01

    Full Text Available In the field of geotechnics, certain vagueness and ambiquity appears. We might not be able to design a mathematically accuratedescription of rock, whose properties change during the excavation (rock strength, discontinuities direction, dislocations, rock type.Furthermore, the excavation regime (thrust, revolutions, torque changes too, as well as the edge angle of cutting tools (due to wear andworking ability of cutterhead as result of sequential exchanges of worn-out cutterhead discs. All of these facts cause that the cutterheadoperates using the discs with different wear stage. The above mentioned problems led us to the decision to use the fuzzy logic and fuzzy sets,e.g. techniques operating with vagueness and ambiguity.

  10. Fuzzy C-Means Clustering Model Data Mining For Recognizing Stock Data Sampling Pattern

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    Sylvia Jane Annatje Sumarauw

    2007-06-01

    Full Text Available Abstract Capital market has been beneficial to companies and investor. For investors, the capital market provides two economical advantages, namely deviden and capital gain, and a non-economical one that is a voting .} hare in Shareholders General Meeting. But, it can also penalize the share owners. In order to prevent them from the risk, the investors should predict the prospect of their companies. As a consequence of having an abstract commodity, the share quality will be determined by the validity of their company profile information. Any information of stock value fluctuation from Jakarta Stock Exchange can be a useful consideration and a good measurement for data analysis. In the context of preventing the shareholders from the risk, this research focuses on stock data sample category or stock data sample pattern by using Fuzzy c-Me, MS Clustering Model which providing any useful information jar the investors. lite research analyses stock data such as Individual Index, Volume and Amount on Property and Real Estate Emitter Group at Jakarta Stock Exchange from January 1 till December 31 of 204. 'he mining process follows Cross Industry Standard Process model for Data Mining (CRISP,. DM in the form of circle with these steps: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. At this modelling process, the Fuzzy c-Means Clustering Model will be applied. Data Mining Fuzzy c-Means Clustering Model can analyze stock data in a big database with many complex variables especially for finding the data sample pattern, and then building Fuzzy Inference System for stimulating inputs to be outputs that based on Fuzzy Logic by recognising the pattern. Keywords: Data Mining, AUz..:y c-Means Clustering Model, Pattern Recognition

  11. Fuzzy and interval finite element method for heat conduction problem

    CERN Document Server

    Majumdar, Sarangam; Chakraverty, S

    2012-01-01

    Traditional finite element method is a well-established method to solve various problems of science and engineering. Different authors have used various methods to solve governing differential equation of heat conduction problem. In this study, heat conduction in a circular rod has been considered which is made up of two different materials viz. aluminum and copper. In earlier studies parameters in the differential equation have been taken as fixed (crisp) numbers which actually may not. Those parameters are found in general by some measurements or experiments. So the material properties are actually uncertain and may be considered to vary in an interval or as fuzzy and in that case complex interval arithmetic or fuzzy arithmetic has to be considered in the analysis. As such the problem is discretized into finite number of elements which depend on interval/fuzzy parameters. Representation of interval/fuzzy numbers may give the clear picture of uncertainty. Hence interval/fuzzy arithmetic is applied in the fin...

  12. 集群资源模糊聚类划分模型%Fuzzy Clustering Partition Model of Cluster Resource

    Institute of Scientific and Technical Information of China (English)

    那丽春

    2012-01-01

    A fuzzy clustering partition model of cluster resource is proposed in this paper. It quantizes and normalizes the computer resource parameters of CPU, memory, I/O, network adapter and net. It uses fuzzy clustering technique to realize the partition of the computing nodes in the computer clusters. By using of the vector of resource demand and the vector of lowest inaccuracy tolerance, it can divide the computer cluster into several classes and the performance of these computers in one class is more similar. Test results show that this model can effectively partition the computer cluster and it fits the resource schedule of cloud computing.%提出一种集群资源模糊聚类划分模型.对计算机集群中计算节点的CPU、内存、网络、I/O和网卡资源参数进行量化和规范化,运用模糊聚类技术,实现计算节点的聚类划分.引入任务资源需求向量和最低误差容忍向量,将计算机集群划分为若干个性能均衡的逻辑子群.测试结果表明,该模型能有效划分计算机集群,适用于云计算领域的资源调度.

  13. A Hybrid Technique Based on Combining Fuzzy K-means Clustering and Region Growing for Improving Gray Matter and White Matter Segmentation

    Directory of Open Access Journals (Sweden)

    Ashraf Afifi

    2012-07-01

    Full Text Available In this paper we present a hybrid approach based on combining fuzzy k-means clustering, seed region growing, and sensitivity and specificity algorithms to measure gray (GM and white matter (WM tissue. The proposed algorithm uses intensity and anatomic information for segmenting of MRIs into different tissue classes, especially GM and WM. It starts by partitioning the image into different clusters using fuzzy k-means clustering. The centers of these clusters are the input to the region growing (SRG method for creating the closed regions. The outputs of SRG technique are fed to sensitivity and specificity algorithm to merge the similar regions in one segment. The proposed algorithm is applied to challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI datasets. The experimental results show that the proposed technique produces accurate and stable results.

  14. Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture

    Science.gov (United States)

    Sanfilippo, Antonio [Richland, WA; Calapristi, Augustin J [West Richland, WA; Crow, Vernon L [Richland, WA; Hetzler, Elizabeth G [Kennewick, WA; Turner, Alan E [Kennewick, WA

    2009-12-22

    Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture are described. In one aspect, a document clustering method includes providing a document set comprising a plurality of documents, providing a cluster comprising a subset of the documents of the document set, using a plurality of terms of the documents, providing a cluster label indicative of subject matter content of the documents of the cluster, wherein the cluster label comprises a plurality of word senses, and selecting one of the word senses of the cluster label.

  15. Bilateral Filtering using Modified Fuzzy Clustering for Image Denoising

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    G.Vijaya,

    2011-01-01

    Full Text Available This paper presents a novel bilateral filtering using weighed fcm algorithm based on Gaussian kernel unction for image manipulations such as segmentation and denoising . Our proposed bilateral filteringconsists of the standard bilateral filter and the original Euclidean distance is replaced by a kernel – induced distance in the algorithm. We have applied the proposed filtering for image denoising with both the impulse and Gaussian random noise, which achieves better results than the bilateral filtering based denoising approaches, the Perona-Maliks anisotropic diffusion filter, the fuzzy vector median filter and the Non-Local Means filter.

  16. Classifying OECD Countries According to Health Indicators Using Fuzzy Clustering Ana lysis

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    Nesrin Alptekin

    2015-12-01

    Full Text Available This study was conducted in order to classify OECD countries according to health indicators using fuzzy clustering analysis, to identify the cluster in which Turkey is in and the other countries located in the same cluster with Turkey and to determine whether Turkey shows similar characteristics with other countries located in the same cluster or not. In the study, 34 OECD member countries were discussed. With ten variables that directly and indirectly affect the health, c- means clustering analysis was performed. The NCSS 10 software package was used to analyze the data.In the analysis, it was determined that the most appropriate cluster number is five; three countries involved in the first cluster, nine countries involved in the second cluster, nine countries involved in the third cluster, six countries involved in the fourth cluster and seven countries involved in the fifth cluster. Turkey is located in the fourth cluster. Other countries in the same cluster along with Turkey are Estonia, Hungary, Mexico, Poland and Chile

  17. A peculiar object in M 51: fuzzy star cluster or a background galaxy?

    Science.gov (United States)

    Scheepmaker, R. A.; Lamers, H. J. G. L. M.; Larsen, S. S.; Anders, P.

    2008-01-01

    Aims: We study a peculiar object with a projected position close to the nucleus of M 51. It is unusually large for a star cluster in M 51 and we therefore investigate the three most likely options to explain this object: (a) a background galaxy, (b) a cluster in the disk of M 51 and (c) a cluster in M 51, but in front of the disk. Methods: We use broad-band images of the Advanced Camera for Surveys and the Near Infrared Camera and Multi-Object Spectrometer, both on board the Hubble Space Telescope, to study the properties of this object. Assuming the object is a star cluster, we fit the metallicity, age, mass and extinction using simple stellar population models. Assuming the object is a background galaxy, we estimate the extinction from the colour of the background around the object. We study the structural parameters of the object by fitting the spatial profile with analytical models. Results: We find de-reddened colours of the object which are bluer than expected for a typical elliptical galaxy, and the central surface brightness is brighter than the typical surface brightness of a disc galaxy. It is therefore not likely that the object is a background galaxy. Assuming the object is a star cluster in the disc of M 51, we estimate an age and mass of 0.7+0.1-0.1 Gyr and 2.2+0.3-0.3× 105~M⊙, respectively (with the extinction fixed to E(B-V)= 0.2). Considering the large size of the object, we argue that in this scenario we observe the cluster just prior to final dissolution. If we fit for the extinction as a free parameter, a younger age is allowed and the object is not close to final dissolution. Alternatively, the object could be a star cluster in M 51, but in front of the disc, with an age of 1.4+0.5-0.2 Gyr, mass M = 1.7+0.8-0.3× 105~M⊙. Its effective radius is between ~12-25 pc. This makes the object a “fuzzy star cluster”, raising the issue of how an object of this age would end up outside the disc. Based on observations made with the NASA/ESA Hubble

  18. An Adaptive Fuzzy Clustering and Location Management in Mobile Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Obulla Reddy

    2012-11-01

    Full Text Available In the typical Ad Hoc networks application, the network hosts usually perform the given task according to groups, e.g. the command and control over staff and accruement in military affairs, traffic management, etc. Therefore, it is very significant for the study of multicast routing protocols of the Ad Hoc networks. Multicast protocols in MANETs must consider control overhead for maintenance, energy efficiency of nodes and routing trees managements to frequent changes of network topology. Now-a days Multicast protocols extended with Cluster based approach. Cluster based multicast tree formation is still research issues. The mobility of nodes will always increase the communication delay because of re-clustering and cluster head selections. For this issue we evaluate Adaptive Fuzzy System (AFS to multicast communication in mobile ad hoc networks (MANETs. To evaluate the performance of AFS, we simulate the fuzzy clustering in a variety of mobile network topologies in NS-2 and compare it with Cluster-based On Demand Multicast Routing Protocol (CODMRP and Cluster-based routing protocol (CBRP. Our simulation result shows the effectiveness and efficiency of AFMR: high packet delivery ratio is achieved while the delay and overhead are the lowest.

  19. Target Recognition Based on Fuzzy Dempster Data Fusion Method

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    Yong Deng

    2010-08-01

    Full Text Available Data fusion technology is widely used in automatic target recognition system. Problems in data fusion system are complex by nature and can often be characterised by not only randomness but also by fuzziness. To accommodate complex natural problems with both types of uncertainties, it is profitable to construct a data fusion structure based on fuzzy set theory and Dempster Shafer evidence theory. In this paper, after representing both, the individual attribute of target in the model database and the sensor observation or report as fuzzy membership function, a likelihood function was constructed to deal with fuzzy data collected by each sensor. The method to determine basic probability assignments of each sensor report is proposed. Sensor reports are fused through classical Dempster combination rule. A numerical example is illustrated to show the target recognition application of the fuzzy-Dempster approach.Defence Science Journal, 2010, 60(5, pp.525-530, DOI:http://dx.doi.org/10.14429/dsj.60.576

  20. Semi-supervised clustering methods

    Science.gov (United States)

    Bair, Eric

    2013-01-01

    Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as “semi-supervised clustering” methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided. PMID:24729830

  1. Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering

    Directory of Open Access Journals (Sweden)

    Oliynyk Andriy

    2012-08-01

    Full Text Available Abstract Background Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Results Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting, which is designed to optimize: (i fast and accurate detection, (ii offline sorting and (iii online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com using LabVIEW (National Instruments, USA. We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is

  2. Causal association rule mining methods based on fuzzy state description

    Institute of Scientific and Technical Information of China (English)

    Liang Kaijian; Liang Quan; Yang Bingru

    2006-01-01

    Aiming at the research that using more new knowledge to develope knowledge system with dynamic accordance, and under the background of using Fuzzy language field and Fuzzy language values structure as description framework, the generalized cell Automation that can synthetically process fuzzy indeterminacy and random indeterminacy and generalized inductive logic causal model is brought forward. On this basis, a kind of the new method that can discover causal association rules is provded. According to the causal information of standard sample space and commonly sample space,through constructing its state (abnormality) relation matrix, causal association rules can be gained by using inductive reasoning mechanism. The estimate of this algorithm complexity is given,and its validity is proved through case.

  3. Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering.

    Science.gov (United States)

    Sopharak, Akara; Uyyanonvara, Bunyarit; Barman, Sarah

    2009-01-01

    Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM clustering method. The first result is then fine-tuned with morphological techniques. The detection results are validated by comparing with expert ophthalmologists' hand-drawn ground-truths. Sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are used to evaluate overall performance. It is found that the proposed method detects exudates successfully with sensitivity, specificity, PPV, PLR and accuracy of 87.28%, 99.24%, 42.77%, 224.26 and 99.11%, respectively.

  4. FUZZY CLUSTERING BASED BAYESIAN FRAMEWORK TO PREDICT MENTAL HEALTH PROBLEMS AMONG CHILDREN

    Directory of Open Access Journals (Sweden)

    M R Sumathi

    2017-04-01

    Full Text Available According to World Health Organization, 10-20% of children and adolescents all over the world are experiencing mental disorders. Correct diagnosis of mental disorders at an early stage improves the quality of life of children and avoids complicated problems. Various expert systems using artificial intelligence techniques have been developed for diagnosing mental disorders like Schizophrenia, Depression, Dementia, etc. This study focuses on predicting basic mental health problems of children, like Attention problem, Anxiety problem, Developmental delay, Attention Deficit Hyperactivity Disorder (ADHD, Pervasive Developmental Disorder(PDD, etc. using the machine learning techniques, Bayesian Networks and Fuzzy clustering. The focus of the article is on learning the Bayesian network structure using a novel Fuzzy Clustering Based Bayesian network structure learning framework. The performance of the proposed framework was compared with the other existing algorithms and the experimental results have shown that the proposed framework performs better than the earlier algorithms.

  5. An Algorithm for Detecting the Principal Allotment among Fuzzy Clusters and Its Application as a Technique of Reduction of Analyzed Features Space Dimensionality

    Directory of Open Access Journals (Sweden)

    Dmitri A. Viattchenin

    2009-06-01

    Full Text Available This paper describes a modification of a possibilistic clustering method based on the concept of allotment among fuzzy clusters. Basic ideas of the method are considered and the concept of a principal allotment among fuzzy clusters is introduced. The paper provides the description of the plan of the algorithm for detection principal allotment. An analysis of experimental results of the proposed algorithm’s application to the Tamura’s portrait data in comparison with the basic version of the algorithm and with the NERFCM-algorithm is carried out. A methodology of the algorithm’s application to the dimensionality reduction problem is outlined and the application of the methodology is illustrated on the example of Anderson’s Iris data in comparison with the result of principal component analysis. Preliminary conclusions are formulated also.

  6. Financial Performance Evaluation of Turkish Energy Companies with Fuzzy AHP and Fuzzy TOPSIS Methods

    Directory of Open Access Journals (Sweden)

    Kemal Eyuboglu

    2016-07-01

    Full Text Available Turkey’s economy has expanded in recent years with the increase in energy consumption. Energy is a key input in production and plays a crucial role in the development of an economy. Energy sector interacts with other sectors hence the performances of energy firms are inevitable to follow-up. In the study thirteen energy firms are evaluated with 5 main and 15 sub-criteria for the period of 2008-2013. The 15 sub-criteria are classified in the following main criteria: liquidity, activity, financial leverage, profitability and growth ratios. The weights of the ratios are determined by Fuzzy AHP and then Fuzzy TOPSIS method is used for the rankings of the energy firms. Traditional multi-criteria decision making methods are not used in this study, due to the fact that they are insufficient under uncertainty. After 2008 global financial crisis, the uncertainty has increased all over the world hence the usage of fuzzy methods can provide better results under these conditions. Findings show that Avrasya Oil, Turcas and Aksu have the highest ranking.

  7. A fuzzy logic based clustering strategy for improving vehicular ad-hoc network performance

    Indian Academy of Sciences (India)

    Ali Çalhan

    2015-04-01

    This paper aims to improve the clustering of vehicles by using fuzzy logic in Vehicular Ad-Hoc Networks (VANETs) for making the network more robust and scalable. High mobility and scalability are two vital topics to be considered while providing efficient and reliable communication in VANETs. Clustering is of crucial significance in order to cope with the dynamic features of the VANET topologies. Plenty of parameters related to user preferences, network conditions and application requirements such as speed of mobile nodes, distance to cluster head, data rate and signal strength must be evaluated in the cluster head selection process together with the direction parameter for highly dynamic VANET structures. The prominent parameters speed, acceleration, distance and direction information are taken into account as inputs of the proposed cluster head selection algorithm. The simulation results show that developed fuzzy logic (FL) based cluster head selection algorithm (CHSA) has stable performance in various scenarios in VANETs. This study has also shown that the developed CHSAFL satisfies well the highly demanding requirements of both low speed and high speed vehicles on two-way multilane highway

  8. Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

    Science.gov (United States)

    Mitra, Sunanda; Pemmaraju, Surya

    1992-01-01

    Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

  9. 地方产业集群国际化发展风险评价——基于模糊层次分析法的研究%Research on Risk Evaluation of Local Industrial Clusters' Internationalization Based on the Method of Fuzzy AHP

    Institute of Scientific and Technical Information of China (English)

    侯茂章

    2012-01-01

    跨区域实现国际化扩张是当前地方产业集群发展的必然趋势。国际化过程中的风险界定、风险识别、风险因素分析、风险规避等是地方产业集群风险管理的重要内容。采用模糊层次分析法对地方产业集群国际化发展过程中的主要风险进行定量分析,有助于地方产业集群采取有效措施预防、化解各种风险。%Internationalization is the inevitable trend of local industrial clusters at the present time. Risk definition, risk identification, risk analysis are the vital content of risk management in the process of local industrial clusters' internationalization. By using the Fuzzy AHP method, this paper effectively carries out quantitative analysis on the major risks that local industrial clusters face in its internationalization process, which can help local industrial clusters to take effective measures to prevent and resolve these risks.

  10. Fuzzy Automata Induction using Construction Method

    Directory of Open Access Journals (Sweden)

    Mo Z. Wen

    2006-01-01

    Full Text Available Recurrent neural networks have recently been demonstrated to have the ability to learn simple grammars. In particular, networks using second-order units have been successfully at this task. However, it is often difficult to predict the optimal neural network size to induce an unknown automaton from examples. Instead of just adjusting the weights in a network of fixed topology, we adopt the dynamic networks (i.e. the topology and weights can be simultaneously changed during training for this application. We apply the idea of maximizing correlation in the cascade-correlation algorithm to the second-order single-layer recurrent neural network to generate a new construction algorithm and use it to induce fuzzy finite state automata. The experiment indicates that such a dynamic network performs well.

  11. Independent feature subspace iterative optimization based fuzzy clustering for synthetic aperture radar image segmentation

    Science.gov (United States)

    Yu, Hang; Xu, Luping; Feng, Dongzhu; He, Xiaochuan

    2015-01-01

    Synthetic aperture radar (SAR) image segmentation is investigated from feature extraction to algorithm design, which is characterized by two aspects: (1) multiple heterogeneous features are extracted to describe SAR images and the corresponding similarity measures are developed independently to avoid the mutual influences between different features in order to enhance the discriminability of the final similarity between objects. (2) A method called fuzzy clustering based on independent subspace iterative optimization (FCISIO) is proposed. FCISIO integrates multiple features into an objective function which is then iteratively optimized in each feature subspace to obtain final segmentation results. This strategy can protect the distribution structures of the data points in each feature subspace, which realizes an effective way to integrate multiple features of different properties. In order to improve the computation speed and the accuracy of feature description for FCISIO, we design a region merging algorithm before FCISIO which can use many kinds of information to quickly merge regions inside the true segments. Experiments on synthetic and real SAR images show that the proposed method is effective and robust and can obtain good segmentation results with a very short running time.

  12. New Uncertainty Measure of Rough Fuzzy Sets and Entropy Weight Method for Fuzzy-Target Decision-Making Tables

    Directory of Open Access Journals (Sweden)

    Huani Qin

    2014-01-01

    Full Text Available In the rough fuzzy set theory, the rough degree is used to characterize the uncertainty of a fuzzy set, and the rough entropy of a knowledge is used to depict the roughness of a rough classification. Both of them are effective, but they are not accurate enough. In this paper, we propose a new rough entropy of a rough fuzzy set combining the rough degree with the rough entropy of a knowledge. Theoretical studies and examples show that the new rough entropy of a rough fuzzy set is suitable. As an application, we introduce it into a fuzzy-target decision-making table and establish a new method for evaluating the entropy weight of attributes.

  13. Evolutionary Based Type-2 Fuzzy Routing Protocol for Clustered Wireless Sensor

    Directory of Open Access Journals (Sweden)

    Maryam Salehi

    2016-06-01

    Full Text Available Power management is an important issue in wireless sensor network as the sensor nodes are battery-operated devices. For energy efficient data transmission, many routing protocols have been proposed. To achieve energy efficiency in wireless sensor networks, Clustering is an effective approach. In clustering routing protocol, Cluster heads are selected among all nodes within the wireless sensor networkand clusters are formed by assigning each node to the nearest cluster.Energy efficiency, network lifetime and uncertainties are the main drawbacks in clustering routing protocols.In this paper, a new clustering routing protocol named T2FLSBA is introduced to select optimal cluster heads. The proposed protocol is based on type-2 fuzzy logic system. To achieve the best performance based on the application, its parameters are tuned based on bat algorithm.The three important factors- residual energy, the density of neighbour sensor nodes and the distance to sink are taken into consideration as inputs of T2FLSBA protocol to compute the probability of a node to be a candidate cluster head. The simulation results show that the proposed routing protocol outperforms the existing clustering routing protocols in terms of prolonging the network lifetime and energy consumption of sensor nodes.

  14. Fuzzy multiple attribute decision making methods and applications

    CERN Document Server

    Chen, Shu-Jen

    1992-01-01

    This monograph is intended for an advanced undergraduate or graduate course as well as for researchers, who want a compilation of developments in this rapidly growing field of operations research. This is a sequel to our previous works: "Multiple Objective Decision Making--Methods and Applications: A state-of-the-Art Survey" (No.164 of the Lecture Notes); "Multiple Attribute Decision Making--Methods and Applications: A State-of-the-Art Survey" (No.186 of the Lecture Notes); and "Group Decision Making under Multiple Criteria--Methods and Applications" (No.281 of the Lecture Notes). In this monograph, the literature on methods of fuzzy Multiple Attribute Decision Making (MADM) has been reviewed thoroughly and critically, and classified systematically. This study provides readers with a capsule look into the existing methods, their characteristics, and applicability to the analysis of fuzzy MADM problems. The basic concepts and algorithms from the classical MADM methods have been used in the development of the f...

  15. A method for unbalanced transportation problems in fuzzy environment

    Indian Academy of Sciences (India)

    Deepika Rani; T R Gulati; Amit Kumar

    2014-06-01

    In this paper, we consider the fully fuzzy unbalanced transportation problem in which the total availability/production is more than the total demand and propose a method to solve it. Such problems are usually solved by adding a dummy destination. Since the dummy destination has no existence in reality, the excess availability is not transported at all and is held back at one or more origins. The method proposed in this paper gives the additional information that to which of the destination(s) the excess availability be transported for future demand at minimum cost. The advantage of the proposed method over the existing method is that the fuzzy optimal solution obtained does not involve the dummy destination. The method has been illustrated with the help of an example.

  16. Sales Forecasting Based on ERP System through Delphi, fuzzy Clustering and Back-Propagation Neural Networks with adaptive learning rate

    Directory of Open Access Journals (Sweden)

    Attariuas Hicham

    2012-11-01

    Full Text Available In recent years, there has been a strong tendency by companies to use centralized management systems like Enterprise resource planning (ERP. ERP systems offer a comprehensive and simplified process managements and extensive functional coverage. Sales management module is an important element business management of ERP. This paper describes an intelligent hybrid sales forecasting system ERP-FCBPN sales forecast based on architecture of ERP through Delphi, fuzzy clustering and Back-propagation (BP Neural Networks with adaptive learning rate (FCBPN. The proposed approach is composed of three stages: (1 Stage of data collection: Data collection will be implemented from the fields (attributes existing at the interfaces (Tables the database of the ERP. Collection of Key factors that influence sales be made using the Delphi method; (2 Stage of Data preprocessing: Winter Exponential Smoothing method will be utilized to take the trend effect into consideration. (3 Stage of learning by FCBPN: We use hybrid sales forecasting system based on Delphi, fuzzy clustering and Back-propagation (BP Neural Networks with adaptive learning rate (FCBPN. The data for this study come from an industrial company that manufactures packaging. Experimental results show that the proposed model outperforms the previous and traditional approaches. Therefore, it is a very promising solution for industrial forecasting.

  17. SPATIAL CORRELATION DESCRIPTION OF DEFORMATION OBJECT BASED ON FUZZY CLUSTERING AND GEOLOGICAL ANALYSIS

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The methods of deformation analysis and modeling at single point are realized easily now,but available approaches do not make full use of the information from monitoring points and can not reveal integrated deformation regularity of a deformable body.This paper presents a fuzzy clusetering method to analyze the correlative relations of multiple points in space,and then the spatial model for a practical dangerous rockmass in the area of Three Gorges,Yangtze River is established,in which the correlation of six points in space is analyzed by geological investigation and fuzzy set theory.

  18. FUZZY BASED CLUSTERING AND ENERGY EFFICIENT ROUTING FOR UNDERWATER WIRELESS SENSOR NETWORKS

    Directory of Open Access Journals (Sweden)

    Sihem Souiki

    2015-03-01

    Full Text Available Underwater Wireless Sensor Network (UWSN is a particular kind of sensor networks which is characterized by using acoustic channels for communication. UWSN is challenged by great issues specially the energy supply of sensor node which can be wasted rapidly by several factors. The most proposed routing protocols for terrestrial sensor networks are not adequate for UWSN, thus new design of routing protocols must be adapted to this constrain. In this paper we propose two new clustering algorithms based on Fuzzy C-Means mechanisms. In the first proposition, the cluster head is elected initially based on the closeness to the center of the cluster, then the node having the higher residual energy elects itself as a cluster head. All non-cluster head nodes transmit sensed data to the cluster head. This latter performs data aggregation and transmits the data directly to the base station. The second algorithm uses the same principle in forming clusters and electing cluster heads but operates in multi-hop mode to forward data from cluster heads to the underwater sink (uw-sink. Furthermore the two proposed algorithms are tested for static and dynamic deployment. Simulation results demonstrate the effectiveness of the proposed algorithms resulting in an extension of the network lifetime.

  19. Extended Traffic Crash Modelling through Precision and Response Time Using Fuzzy Clustering Algorithms Compared with Multi-layer Perceptron

    Directory of Open Access Journals (Sweden)

    Iman Aghayan

    2012-11-01

    Full Text Available This paper compares two fuzzy clustering algorithms – fuzzy subtractive clustering and fuzzy C-means clustering – to a multi-layer perceptron neural network for their ability to predict the severity of crash injuries and to estimate the response time on the traffic crash data. Four clustering algorithms – hierarchical, K-means, subtractive clustering, and fuzzy C-means clustering – were used to obtain the optimum number of clusters based on the mean silhouette coefficient and R-value before applying the fuzzy clustering algorithms. The best-fit algorithms were selected according to two criteria: precision (root mean square, R-value, mean absolute errors, and sum of square error and response time (t. The highest R-value was obtained for the multi-layer perceptron (0.89, demonstrating that the multi-layer perceptron had a high precision in traffic crash prediction among the prediction models, and that it was stable even in the presence of outliers and overlapping data. Meanwhile, in comparison with other prediction models, fuzzy subtractive clustering provided the lowest value for response time (0.284 second, 9.28 times faster than the time of multi-layer perceptron, meaning that it could lead to developing an on-line system for processing data from detectors and/or a real-time traffic database. The model can be extended through improvements based on additional data through induction procedure.

  20. Ranking Fuzzy Numbers with a Distance Method using Circumcenter of Centroids and an Index of Modality

    Directory of Open Access Journals (Sweden)

    P. Phani Bushan Rao

    2011-01-01

    Full Text Available Ranking fuzzy numbers are an important aspect of decision making in a fuzzy environment. Since their inception in 1965, many authors have proposed different methods for ranking fuzzy numbers. However, there is no method which gives a satisfactory result to all situations. Most of the methods proposed so far are nondiscriminating and counterintuitive. This paper proposes a new method for ranking fuzzy numbers based on the Circumcenter of Centroids and uses an index of optimism to reflect the decision maker's optimistic attitude and also an index of modality that represents the neutrality of the decision maker. This method ranks various types of fuzzy numbers which include normal, generalized trapezoidal, and triangular fuzzy numbers along with crisp numbers with the particularity that crisp numbers are to be considered particular cases of fuzzy numbers.

  1. Fuzzy-Based XML Knowledge Retrieval Methods in Edaphology

    Directory of Open Access Journals (Sweden)

    K. Naresh kumar

    2016-05-01

    Full Text Available In this paper, we propose a proficient method for knowledge management in Edaphology to assist the edaphologists and those related with agriculture in a big way. The proposed method mainly consists two sections of which the first one is to build the knowledge base using XML and the latter part deals with information retrieval by searching using fuzzy. Initially, the relational database is converted to the XML database. The paper discusses two algorithms, one is when the soil characteristics are inputted to have the plant list and in the other, plant names are inputted to have the soil characteristics suited for the plant. While retrieving the query result, the crisp numerical values are converted to fuzzy using the triangular fuzzy membership function and matched to those in database. And those which satisfy are added to the result list and subsequently the frequency is found out to rank the result list so as to obtain the final sorted list. Performance metrics used in order to evaluate the method and compare it to baseline paper are number of plants retrieved, ranking efficiency, and computation time and memory usage. Results obtained proved the validity of the method and the method obtained average computation time of 0.102 seconds and average memory usage of 2486 Kb, which all are far better than the previous method results.

  2. Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction

    Energy Technology Data Exchange (ETDEWEB)

    Tsantis, Stavros [Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504 (Greece); Spiliopoulos, Stavros; Karnabatidis, Dimitrios [Department of Radiology, School of Medicine, University of Patras, Rion, GR 26504 (Greece); Skouroliakou, Aikaterini [Department of Energy Technology Engineering, Technological Education Institute of Athens, Athens 12210 (Greece); Hazle, John D. [Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States); Kagadis, George C., E-mail: gkagad@gmail.com, E-mail: George.Kagadis@med.upatras.gr, E-mail: GKagadis@mdanderson.org [Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States)

    2014-07-15

    Purpose: Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm. Methods: The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image. Results: A total of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists’ qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures. Conclusions: A

  3. Face recognition using fuzzy integral and wavelet decomposition method.

    Science.gov (United States)

    Kwak, Keun-Chang; Pedrycz, Witold

    2004-08-01

    In this paper, we develop a method for recognizing face images by combining wavelet decomposition, Fisherface method, and fuzzy integral. The proposed approach is comprised of four main stages. The first stage uses the wavelet decomposition that helps extract intrinsic features of face images. As a result of this decomposition, we obtain four subimages (namely approximation, horizontal, vertical, and diagonal detailed images). The second stage of the approach concerns the application of the Fisherface method to these four decompositions. The choice of the Fisherface method in this setting is motivated by its insensitivity to large variation in light direction, face pose, and facial expression. The two last phases are concerned with the aggregation of the individual classifiers by means of the fuzzy integral. Both Sugeno and Choquet type of fuzzy integral are considered as the aggregation method. In the experiments we use n-fold cross-validation to assure high consistency of the produced classification outcomes. The experimental results obtained for the Chungbuk National University (CNU) and Yale University face databases reveal that the approach presented in this paper yields better classification performance in comparison to the results obtained by other classifiers.

  4. Numerical solution of fuzzy boundary value problems using Galerkin method

    Indian Academy of Sciences (India)

    SMITA TAPASWINI; S CHAKRAVERTY; JUAN J NIETO

    2017-01-01

    This paper proposes a new technique based on Galerkin method for solving nth order fuzzy boundary value problem. The proposed method has been illustrated by considering three different cases depending upon the sign of coefficients with benchmark example problems. To show the applicability of the proposed method, an application problem related to heat conduction has also been studied. The results obtained by the proposed methods are compared with the exact solution and other existing methods for demonstrating the validity and efficiency of the present method.

  5. Navigation Algorithm Using Fuzzy Control Method in Mobile Robotics

    Directory of Open Access Journals (Sweden)

    Cviklovič Vladimír

    2016-03-01

    Full Text Available The issue of navigation methods is being continuously developed globally. The aim of this article is to test the fuzzy control algorithm for track finding in mobile robotics. The concept of an autonomous mobile robot EN20 has been designed to test its behaviour. The odometry navigation method was used. The benefits of fuzzy control are in the evidence of mobile robot’s behaviour. These benefits are obtained when more physical variables on the base of more input variables are controlled at the same time. In our case, there are two input variables - heading angle and distance, and two output variables - the angular velocity of the left and right wheel. The autonomous mobile robot is moving with human logic.

  6. Fuzzy Interpolation and Other Interpolation Methods Used in Robot Calibrations

    Directory of Open Access Journals (Sweden)

    Ying Bai

    2012-01-01

    Full Text Available A novel interpolation algorithm, fuzzy interpolation, is presented and compared with other popular interpolation methods widely implemented in industrial robots calibrations and manufacturing applications. Different interpolation algorithms have been developed, reported, and implemented in many industrial robot calibrations and manufacturing processes in recent years. Most of them are based on looking for the optimal interpolation trajectories based on some known values on given points around a workspace. However, it is rare to build an optimal interpolation results based on some random noises, and this is one of the most popular topics in industrial testing and measurement applications. The fuzzy interpolation algorithm (FIA reported in this paper provides a convenient and simple way to solve this problem and offers more accurate interpolation results based on given position or orientation errors that are randomly distributed in real time. This method can be implemented in many industrial applications, such as manipulators measurements and calibrations, industrial automations, and semiconductor manufacturing processes.

  7. A Combined Fuzzy-AHP and Fuzzy-GRA Methodology for Hydrogen Energy Storage Method Selection in Turkey

    Directory of Open Access Journals (Sweden)

    Aytac Yildiz

    2013-06-01

    Full Text Available In this paper, we aim to select the most appropriate Hydrogen Energy Storage (HES method for Turkey from among the alternatives of tank, metal hydride and chemical storage, which are determined based on expert opinions and literature review. Thus, we propose a Buckley extension based fuzzy Analytical Hierarchical Process (Fuzzy-AHP and linear normalization based fuzzy Grey Relational Analysis (Fuzzy-GRA combined Multi Criteria Decision Making (MCDM methodology. This combined approach can be applied to a complex decision process, which often makes sense with subjective data or vague information; and used to solve to solve HES selection problem with different defuzzification methods. The proposed approach is unique both in the HES literature and the MCDM literature.

  8. Novel robust approach for constructing Mamdani-type fuzzy system based on PRM and subtractive clustering algorithm

    Institute of Scientific and Technical Information of China (English)

    褚菲; 马小平; 王福利; 贾润达

    2015-01-01

    A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator (partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy system, and an improved subtractive clustering algorithm in the fuzzy-rule-selecting phase. The weights obtained in PRM, which gives protection against noise and outliers, were incorporated into the potential measure of the subtractive cluster algorithm to enhance the robustness of the fuzzy rule cluster process, and a compact Mamdani-type fuzzy system was established after the parameters in the consequent parts of rules were re-estimated by partial least squares (PLS). The main characteristics of the new approach were its simplicity and ability to construct fuzzy system fast and robustly. Simulation and experiment results show that the proposed approach can achieve satisfactory results in various kinds of data domains with noise and outliers. Compared with D-SVD and ARRBFN, the proposed approach yields much fewer rules and less RMSE values.

  9. Comparative Investigation of Guided Fuzzy Clustering and Mean Shift Clustering for Edge Detection in Electrical Resistivity Tomography Images of Mineral Deposits

    Science.gov (United States)

    Ward, Wil; Wilkinson, Paul; Chambers, Jon; Bai, Li

    2014-05-01

    Geophysical surveying using electrical resistivity tomography (ERT) can be used as a rapid non-intrusive method to investigate mineral deposits [1]. One of the key challenges with this approach is to find a robust automated method to assess and characterise deposits on the basis of an ERT image. Recent research applying edge detection techniques has yielded a framework that can successfully locate geological interfaces in ERT images using a minimal assumption data clustering technique, the guided fuzzy clustering method (gfcm) [2]. Non-parametric clustering techniques are statistically grounded methods of image segmentation that do not require any assumptions about the distribution of data under investigation. This study is a comparison of two such methods to assess geological structure based on the resistivity images. In addition to gfcm, a method called mean-shift clustering [3] is investigated with comparisons directed at accuracy, computational expense, and degree of user interaction. Neither approach requires the number of clusters as input (a common parameter and often impractical), rather they are based on a similar theory that data can be clustered based on peaks in the probability density function (pdf) of the data. Each local maximum in these functions represents the modal value of a particular population corresponding to a cluster and as such the data are assigned based on their relationships to these model values. The two methods differ in that gfcm approximates the pdf using kernel density estimation and identifies population means, assigning cluster membership probabilities to each resistivity value in the model based on its distance from the distribution averages. Whereas, in mean-shift clustering, the density function is not calculated, but a gradient ascent method creates a vector that leads each datum towards high density distributions iteratively using weighted kernels to calculate locally dense regions. The only parameter needed in both methods

  10. Fuzzy Methods and Image Fusion in a Digital Image Processing

    Directory of Open Access Journals (Sweden)

    Jaroslav Vlach

    2012-01-01

    Full Text Available Although the basics of image processing were laid more than 50 years ago, significant development occurred mainly in the last 25 years with the entrance of personal computers and today's problems are already very sophisticated and quick. This article is a contribution to the study of the use of fuzzy logic methods and image fusion for image processing using LabVIEW tools for quality management, in this case especially in the jewelry industry.  

  11. Mapping Diversity of Publication Patterns in the Social Sciences and Humanities: An Approach Making Use of Fuzzy Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Frederik T. Verleysen

    2016-11-01

    Full Text Available Purpose: To present a method for systematically mapping diversity of publication patterns at the author level in the social sciences and humanities in terms of publication type, publication language and co-authorship. Design/methodology/approach: In a follow-up to the hard partitioning clustering by Verleysen and Weeren in 2016, we now propose the complementary use of fuzzy cluster analysis, making use of a membership coefficient to study gradual differences between publication styles among authors within a scholarly discipline. The analysis of the probability density function of the membership coefficient allows to assess the distribution of publication styles within and between disciplines. Findings: As an illustration we analyze 1,828 productive authors affiliated in Flanders, Belgium. Whereas a hard partitioning previously identified two broad publication styles, an international one vs. a domestic one, fuzzy analysis now shows gradual differences among authors. Internal diversity also varies across disciplines and can be explained by researchers' specialization and dissemination strategies. Research limitations: The dataset used is limited to one country for the years 2000-2011; a cognitive classification of authors may yield a different result from the affiliation-based classification used here. Practical implications: Our method is applicable to other bibliometric and research evaluation contexts, especially for the social sciences and humanities in non-Anglophone countries. Originality/value: The method proposed is a novel application of cluster analysis to the field of bibliometrics. Applied to publication patterns at the author level in the social sciences and humanities, for the first time it systematically documents intra-disciplinary diversity.

  12. Canopy Spectral Reflectance Characteristics of Rice with Different Cultural Practices and Their Fuzzy Cluster Analysis

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The influence of major cultural practices including different nitrogen application rates, population densities, transplanting leaf ages of seedling, and water regimes on rice canopy spectral reflectance was investigated. Results showed that increased nitrogen rates, water regimes and population densities and decreased seedling ages could enhance reflectance at NIR (near infrared) bands and reduce reflectance at visible bands. Using reflectance of green, red and NIR band and ratio index of 810-560 nm could distinguish the different type of rice by fuzzy cluster analysis.

  13. User preferences-aware recommendation for trustworthy cloud services based on fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    马华; 胡志刚

    2015-01-01

    The cloud computing has been growing over the past few years, and service providers are creating an intense competitive world of business. This proliferation makes it hard for new users to select a proper service among a large amount of service candidates. A novel user preferences-aware recommendation approach for trustworthy services is presented. For describing the requirements of new users in different application scenarios, user preferences are identified by usage preference, trust preference and cost preference. According to the similarity analysis of usage preference between consumers and new users, the candidates are selected, and these data about service trust provided by them are calculated as the fuzzy comprehensive evaluations. In accordance with the trust and cost preferences of new users, the dynamic fuzzy clusters are generated based on the fuzzy similarity computation. Then, the most suitable services can be selected to recommend to new users. The experiments show that this approach is effective and feasible, and can improve the quality of services recommendation meeting the requirements of new users in different scenario.

  14. DCT-Yager FNN: a novel Yager-based fuzzy neural network with the discrete clustering technique.

    Science.gov (United States)

    Singh, A; Quek, C; Cho, S Y

    2008-04-01

    Earlier clustering techniques such as the modified learning vector quantization (MLVQ) and the fuzzy Kohonen partitioning (FKP) techniques have focused on the derivation of a certain set of parameters so as to define the fuzzy sets in terms of an algebraic function. The fuzzy membership functions thus generated are uniform, normal, and convex. Since any irregular training data is clustered into uniform fuzzy sets (Gaussian, triangular, or trapezoidal), the clustering may not be exact and some amount of information may be lost. In this paper, two clustering techniques using a Kohonen-like self-organizing neural network architecture, namely, the unsupervised discrete clustering technique (UDCT) and the supervised discrete clustering technique (SDCT), are proposed. The UDCT and SDCT algorithms reduce this data loss by introducing nonuniform, normal fuzzy sets that are not necessarily convex. The training data range is divided into discrete points at equal intervals, and the membership value corresponding to each discrete point is generated. Hence, the fuzzy sets obtained contain pairs of values, each pair corresponding to a discrete point and its membership grade. Thus, it can be argued that fuzzy membership functions generated using this kind of a discrete methodology provide a more accurate representation of the actual input data. This fact has been demonstrated by comparing the membership functions generated by the UDCT and SDCT algorithms against those generated by the MLVQ, FKP, and pseudofuzzy Kohonen partitioning (PFKP) algorithms. In addition to these clustering techniques, a novel pattern classifying network called the Yager fuzzy neural network (FNN) is proposed in this paper. This network corresponds completely to the Yager inference rule and exhibits remarkable generalization abilities. A modified version of the pseudo-outer product (POP)-Yager FNN called the modified Yager FNN is introduced that eliminates the drawbacks of the earlier network and yi- elds

  15. PARTIAL TRAINING METHOD FOR HEURISTIC ALGORITHM OF POSSIBLE CLUSTERIZATION UNDER UNKNOWN NUMBER OF CLASSES

    Directory of Open Access Journals (Sweden)

    D. A. Viattchenin

    2009-01-01

    Full Text Available A method for constructing a subset of labeled objects which is used in a heuristic algorithm of possible  clusterization with partial  training is proposed in the  paper.  The  method  is  based  on  data preprocessing by the heuristic algorithm of possible clusterization using a transitive closure of a fuzzy tolerance. Method efficiency is demonstrated by way of an illustrative example.

  16. Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy c-Mean Clustering and Advanced Neural Network

    Directory of Open Access Journals (Sweden)

    He Huang

    2013-01-01

    Full Text Available Forecasting of urban traffic flow is important to intelligent transportation system (ITS developments and implementations. The precise forecasting of traffic flow will be pretty helpful to relax road traffic congestion. The accuracy of traditional single model without correction mechanism is poor. Summarizing the existing prediction models and considering the characteristics of the traffic itself, a traffic flow prediction model based on fuzzy c-mean clustering method (FCM and advanced neural network (NN was proposed. FCM can improve the prediction accuracy and robustness of the model, while advanced NN can optimize the generalization ability of the model. Besides these, the output value of the model is calibrated by the correction mechanism. The experimental results show that the proposed method has better prediction accuracy and robustness than the other models.

  17. Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

    Energy Technology Data Exchange (ETDEWEB)

    Nedialkova, Lilia V.; Amat, Miguel A. [Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544 (United States); Kevrekidis, Ioannis G., E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de [Department of Chemical and Biological Engineering and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544 (United States); Hummer, Gerhard, E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de [Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438 Frankfurt am Main (Germany)

    2014-09-21

    Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.

  18. A new robust fuzzy method for unmanned flying vehicle control

    Institute of Scientific and Technical Information of China (English)

    Mojtaba Mirzaei; Mohammad Eghtesad; Mohammad Mahdi Alishahi

    2015-01-01

    A new general robust fuzzy approach was presented to control the position and the attitude of unmanned flying vehicles (UFVs). Control of these vehicles was challenging due to their nonlinear underactuated behaviors. The proposed control system combined great advantages of generalized indirect adaptive sliding mode control (IASMC) and fuzzy control for the UFVs. An on-line adaptive tuning algorithm based on Lyapunov function and Barbalat lemma was designed, thus the stability of the system can be guaranteed. The chattering phenomenon in the sliding mode control was reduced and the steady error was also alleviated. The numerical results, for an underactuated quadcopter and a high speed underwater vehicle as case studies, indicate that the presented adaptive design of fuzzy sliding mode controller performs robustly in the presence of sensor noise and external disturbances. In addition, online unknown parameter estimation of the UFVs, such as ground effect and planing force especially in the cases with the Gaussian sensor noise with zero mean and standard deviation of 0.5 m and 0.1 rad and external disturbances with amplitude of 0.1 m/s2 and frequency of 0.2 Hz, is one of the advantages of this method. These estimated parameters are then used in the controller to improve the trajectory tracking performance.

  19. An introduction to fuzzy linear programming problems theory, methods and applications

    CERN Document Server

    Kaur, Jagdeep

    2016-01-01

    The book presents a snapshot of the state of the art in the field of fully fuzzy linear programming. The main focus is on showing current methods for finding the fuzzy optimal solution of fully fuzzy linear programming problems in which all the parameters and decision variables are represented by non-negative fuzzy numbers. It presents new methods developed by the authors, as well as existing methods developed by others, and their application to real-world problems, including fuzzy transportation problems. Moreover, it compares the outcomes of the different methods and discusses their advantages/disadvantages. As the first work to collect at one place the most important methods for solving fuzzy linear programming problems, the book represents a useful reference guide for students and researchers, providing them with the necessary theoretical and practical knowledge to deal with linear programming problems under uncertainty.

  20. COMPARISON OF FUZZY TOPSIS METHODS USED GROUP DECISION MAKING AND AN APPLICATION

    Directory of Open Access Journals (Sweden)

    FATİH ECER

    2013-06-01

    Full Text Available Fuzzy TOPSIS method used group decision making in fuzzy environment is one of the Multiple Criteria Decision Making (MCDM methods.  It is needed to decision makers (DM, alternatives and decision criteria in order to apply this method. Foundation of the method is the ideal solution is the shortest distance from Fuzzy Positive Ideal Solution (FPIS and the farthest distance from Fuzzy Negative Ideal Solution (FNIS. Using FPIS and FNIS, closeness coefficients of alternatives are evaluated. Closeness coefficients express scores of the alternatives. According to closeness coefficients, alternatives are ranked from the best to the worst. In this study, two fuzzy TOPSIS methods having different algorithms are compared. To this purpose, firstly assessments of decision makers are converted to triangular fuzzy numbers. It is seen at the end of the study that ranking orders of alternatives don’t change.

  1. Freeway incident detection based on improved fuzzy clustering arithmetic and ANFIS%基于改进模糊聚类与ANFIS的高速公路事件检测

    Institute of Scientific and Technical Information of China (English)

    姚磊; 刘渊

    2013-01-01

    为了准确并及时地发现高速公路上的交通事故隐患,减少事故引发的交通延迟,提高高速公路运行安全性,结合减法聚类与模糊C均值(FCM)聚类算法对输入样本数据进行聚类,建成初始模糊推理系统,然后通过神经网络的自学习机制,训练模糊系统参数,确定模糊推理规则,建立最终模糊模型。通过仿真实验结果对比,验证了基于改进模糊聚类与自适应神经模糊推理系统(ANFIS)建模方法的有效性。%In order to accurately and timely detect highway traffic accident, reduce traffic delay and improve highway safety, this paper combines subtractive clustering and Fuzzy C-Means(FCM) clustering method to cluster the input sample data to build the initial fuzzy inference system, then the hybrid algorithm is used to train the parameters of the fuzzy system, determine the fuzzy reasoning rules, and establish a final training fuzzy model. Compared with the simulation experimental results, the method obtains excellent performance on ROC(Receiver Operation Characteristic)curve, shows the validity of the modeling method based on the improved fuzzy clustering and Adaptive Neural Fuzzy Inference System(ANFIS).

  2. Uncovering and testing the fuzzy clusters based on lumped Markov chain in complex network.

    Science.gov (United States)

    Jing, Fan; Jianbin, Xie; Jinlong, Wang; Jinshuai, Qu

    2013-01-01

    Identifying clusters, namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. By means of a lumped Markov chain model of a random walker, we propose two novel ways of inferring the lumped markov transition matrix. Furthermore, some useful results are proposed based on the analysis of the properties of the lumped Markov process. To find the best partition of complex networks, a novel framework including two algorithms for network partition based on the optimal lumped Markovian dynamics is derived to solve this problem. The algorithms are constructed to minimize the objective function under this framework. It is demonstrated by the simulation experiments that our algorithms can efficiently determine the probabilities with which a node belongs to different clusters during the learning process and naturally supports the fuzzy partition. Moreover, they are successfully applied to real-world network, including the social interactions between members of a karate club.

  3. Fuzzy cluster analysis of the provenance of ancient Yaozhou porcelain body

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    The technique of neutron activation analysis (NAA) has been employed to measure the content of 29 kinds of elements in the sample of Yaozhou porcelain bodies. Then a fuzzy cluster analysis has been conducted to the NAA data and a diagram of the dynamic fuzzy cluster analysis has been achieved. The results indicate that the batch of ancient Yaozhou porcelain bodies, which were of different overglaze color and were produced by different kilns during a period of over 800 years from the Tang Dynasty (618-907 A.D.) to the Yuan Dynasty (1271-1368 A.D.), has shared a stable and concentrated raw material source. Provenances of porcelain bodies from different times, though having their specific independence, enjoy a close relationship and are not far from one another. Provenances of porcelain bodies made during the Tang Dynasty and the Five Dynasties (907-960 A.D.) are found to be closer to one another, while those of the Song (960-1279 A.D.) and the Jin Dynasty (1115-1234 A.D.) are comparatively concentrated in certain areas and are different from those of the Tang Dynasty. Both the tri-colored glazed pottery made in Yaozhou kilns during the Tang Dynasty and the Yaozhou porcelain bodies of the Tang period are from the same provenance.

  4. Improving of Business Planning Using the Method of Fuzzy Numbers

    Directory of Open Access Journals (Sweden)

    Iryna Kosteteska

    2016-03-01

    Full Text Available Purpose: Summarize the experience of using modern methods in the business plan with the application of economic and mathematical modeling. Methodology: Theoretical and methodological basis of the study is the basic principles of economic theory, agricultural economics and scientific research of leading home and foreign scholars on the theory of planning. Originality: This further justifies business planning processes in agriculture from the standpoint of raising economic protection of farmers. The methodology for assessing farm income for planned indicators through the application of fuzzy numbers method in business planning is improved.

  5. Optimizing the atmospheric sampling sites using fuzzy mathematic methods

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    A new approach applying fuzzy mathematic theorems, including the Primary Matrix Element Theorem and the Fisher ClassificationMethod, was established to solve the optimization problem of atmospheric environmental sampling sites. According to its basis, an applicationin the optimization of sampling sites in the atmospheric environmental monitoring was discussed. The method was proven to be suitable andeffective. The results were admitted and applied by the Environmental Protection Bureau (EPB) of many cities of China. A set of computersoftware of this approach was also completely compiled and used.

  6. Revised Max-Min Average Composition Method for Decision Making Using Intuitionistic Fuzzy Soft Matrix Theory

    Directory of Open Access Journals (Sweden)

    P. Shanmugasundaram

    2014-01-01

    Full Text Available In this paper a revised Intuitionistic Fuzzy Max-Min Average Composition Method is proposed to construct the decision method for the selection of the professional students based on their skills by the recruiters using the operations of Intuitionistic Fuzzy Soft Matrices. In Shanmugasundaram et al. (2014, Intuitionistic Fuzzy Max-Min Average Composition Method was introduced and applied in Medical diagnosis problem. Sanchez’s approach (Sanchez (1979 for decision making is studied and the concept is modified for the application of Intuitionistic fuzzy soft set theory. Through a survey, the opportunities and selection of the students with the help of Intuitionistic fuzzy soft matrix operations along with Intuitionistic fuzzy max-min average composition method is discussed.

  7. FRCA: a fuzzy relevance-based cluster head selection algorithm for wireless mobile ad-hoc sensor networks.

    Science.gov (United States)

    Lee, Chongdeuk; Jeong, Taegwon

    2011-01-01

    Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

  8. Numerical Investigations on Hybrid Fuzzy Fractional Differential Equations by Improved Fractional Euler Method

    Directory of Open Access Journals (Sweden)

    D. Vivek

    2016-11-01

    Full Text Available In this paper, the improved Euler method is used for solving hybrid fuzzy fractional differential equations (HFFDE of order $q \\in (0, 1 $ under Caputo-type fuzzy fractional derivatives. This method is based on the fractional Euler method and generalized Taylor's formula. The accuracy and efficiency of the proposed method is demonstrated by solving numerical examples.

  9. New Image Recognition Method Based on Rough-Sets and Fuzzy Theory

    Institute of Scientific and Technical Information of China (English)

    张艳; 李凤霞; 战守义

    2003-01-01

    A new image recognition method based on fuzzy-rough sets theory is proposed, and its implementation discussed. The performance of this method as applied to ferrography image recognition is evaluated. It is shown that the new method gives better results than fuzzy or rough-sets method when used alone.

  10. Real-time monitoring of abnormal conditions based on Fuzzy Kohonen clustering network in gas metal arc welding

    Institute of Scientific and Technical Information of China (English)

    GAO Jinqiang; WU Chuansong; HU Jiakun

    2007-01-01

    A real-time monitoring system based on through-the-arc sensing is developed for detecting abnormal conditions in gas metal arc welding. The transient signals of welding voltage and current during the welding process are sampled and processed by statistical analysis methods. It is found that three statistical parameters (the standard deviation,variance, and kurtosis of welding current) show obvious variations during the step disturbance, which is intentionally introduced into the T-joint test pieces by cutting a gap in the vertical plane. A Fuzzy Kohonen clustering network (FKCN) is put forward to monitor the abnormal conditions in real-time. Ten robotic welding experiments are conducted to verify the real-time monitoring system. It is found that the correct identification rate is above 90%.

  11. Alternative Fuzzy Cluster Segmentation of Remote Sensing Images Based on Adaptive Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Jing; TANG Jilong; LIU Jibin; REN Chunying; LIU Xiangnan; FENG Jiang

    2009-01-01

    Remote sensing image segmentation is the basis of image understanding and analysis. However, the precision and the speed of segmentation can not meet the need of image analysis, due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm (AGA) and Alternative Fuzzy C-Means (AFCM). Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased, and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means (FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.

  12. Solid oxide fuel cell anode image segmentation based on a novel quantum-inspired fuzzy clustering

    Science.gov (United States)

    Fu, Xiaowei; Xiang, Yuhan; Chen, Li; Xu, Xin; Li, Xi

    2015-12-01

    High quality microstructure modeling can optimize the design of fuel cells. For three-phase accurate identification of Solid Oxide Fuel Cell (SOFC) microstructure, this paper proposes a novel image segmentation method on YSZ/Ni anode Optical Microscopic (OM) images. According to Quantum Signal Processing (QSP), the proposed approach exploits a quantum-inspired adaptive fuzziness factor to adaptively estimate the energy function in the fuzzy system based on Markov Random Filed (MRF). Before defuzzification, a quantum-inspired probability distribution based on distance and gray correction is proposed, which can adaptively adjust the inaccurate probability estimation of uncertain points caused by noises and edge points. In this study, the proposed method improves accuracy and effectiveness of three-phase identification on the micro-investigation. It provides firm foundation to investigate the microstructural evolution and its related properties.

  13. Fuzzy approach to analysis of flood risk based on variable fuzzy sets and improved information diffusion methods

    Directory of Open Access Journals (Sweden)

    Q. Li

    2013-02-01

    Full Text Available The predictive analysis of natural disasters and their consequences is challenging because of uncertainties and incomplete data. The present article studies the use of variable fuzzy sets (VFS and improved information diffusion method (IIDM to construct a composite method. The proposed method aims to integrate multiple factors and quantification of uncertainties within a consistent system for catastrophic risk assessment. The fuzzy methodology is proposed in the area of flood disaster risk assessment to improve probability estimation. The purpose of the current study is to establish a fuzzy model to evaluate flood risk with incomplete data sets. The results of the example indicate that the methodology is effective and practical; thus, it has the potential to forecast the flood risk in flood risk management.

  14. Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing

    Directory of Open Access Journals (Sweden)

    Liao Chun-Chih

    2011-08-01

    Full Text Available Abstract Background In recent years, magnetic resonance imaging (MRI has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images. This paper uses an algorithm integrating fuzzy-c-mean (FCM and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain. Methods The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT on a pixel level. Overall data were then evaluated using a quantified system. Results The quantified parameters, including the "percent match" (PM and "correlation ratio" (CR, suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain. Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related. Conclusions Results indicated

  15. Knowledge resources fuzzy clustering analysis based on ontology%基于本体的知识资源模糊聚类分析

    Institute of Scientific and Technical Information of China (English)

    李广明

    2011-01-01

    This paper proposed an ontology-based knowledge resources fuzzy clustering method, through decomposed ontology, calculated element space vector, calculated the semantic distance between knowledge ontology to express fuzzy similarity,at last, to process fuzzy clustering. Finally proposed examples which demonstrated the method.%针对知识资源分块的边界模糊性的特点,以知识资源的本体表示为基础,应用基于本体的知识资源模糊聚类方法,通过分解知识本体,计算概念、属性、关系、结构等知识构成元素的空间向量,结合WordNet的词汇映射关系,使用知识本体间的语义距离表示知识资源间的模糊相似度,来实现知识本体的模糊聚类.最后举出实例来描述本文应用的方法,说明模糊聚类方法可以较好地分类知识资源.

  16. Evaluation of fuzzy relation method for medical decision support.

    Science.gov (United States)

    Wagholikar, Kavishwar; Mangrulkar, Sanjeev; Deshpande, Ashok; Sundararajan, Vijayraghavan

    2012-02-01

    The potential of computer based tools to assist physicians in medical decision making, was envisaged five decades ago. Apart from factors like usability, integration with work-flow and natural language processing, lack of decision accuracy of the tools has hindered their utility. Hence, research to develop accurate algorithms for medical decision support tools, is required. Pioneering research in last two decades, has demonstrated the utility of fuzzy set theory for medical domain. Recently, Wagholikar and Deshpande proposed a fuzzy relation based method (FR) for medical diagnosis. In their case studies for heart and infectious diseases, the FR method was found to be better than naive bayes (NB). However, the datasets in their studies were small and included only categorical symptoms. Hence, more evaluative studies are required for drawing general conclusions. In the present paper, we compare the classification performance of FR with NB, for a variety of medical datasets. Our results indicate that the FR method is useful for classification problems in the medical domain, and that FR is marginally better than NB. However, the performance of FR is significantly better for datasets having high proportion of unknown attribute values. Such datasets occur in problems involving linguistic information, where FR can be particularly useful. Our empirical study will benefit medical researchers in the choice of algorithms for decision support tools.

  17. Simulation of thermal behavior of residential buildings using fuzzy active learning method

    OpenAIRE

    Masoud Taheri Shahraein; Hamid Taheri Shahraiyni; Melika Sanaeifar

    2015-01-01

    In this paper, a fuzzy modeling technique called Modified Active Learning Method (MALM) was introduced and utilized for fuzzy simulation of indoor and inner surface temperatures in residential buildings using meteorological data and its capability for fuzzy simulation was compared with other studies. The case studies for simulations were two residential apartments in the Fakouri and Rezashahr neighborhoods of Mashhad, Iran. The hourly inner surface and indoor temperature data were accumulated...

  18. AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number

    Directory of Open Access Journals (Sweden)

    Cooper James B

    2010-03-01

    Full Text Available Abstract Background Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry. Results We integrated strategies from machine learning, cartography, and graph theory into a new informatics method for automatically clustering self-organizing map ensembles of high-dimensional data. Our new method, called AutoSOME, readily identifies discrete and fuzzy data clusters without prior knowledge of cluster number or structure in diverse datasets including whole genome microarray data. Visualization of AutoSOME output using network diagrams and differential heat maps reveals unexpected variation among well-characterized cancer cell lines. Co-expression analysis of data from human embryonic and induced pluripotent stem cells using AutoSOME identifies >3400 up-regulated genes associated with pluripotency, and indicates that a recently identified protein-protein interaction network characterizing pluripotency was underestimated by a factor of four. Conclusions By effectively extracting important information from high-dimensional microarray data without prior knowledge or the need for data filtration, AutoSOME can yield systems-level insights from whole genome microarray expression studies. Due to its generality, this new method should also have practical utility for a variety of data-intensive applications, including the results of deep sequencing experiments. AutoSOME is available for download at http://jimcooperlab.mcdb.ucsb.edu/autosome.

  19. Analysis of performance measures with single channel fuzzy queues under two class by using ranking method

    Science.gov (United States)

    Mueen, Zeina; Ramli, Razamin; Zaibidi, Nerda Zura

    2016-08-01

    In this paper, we propose a procedure to find different performance measurements under crisp value terms for new single fuzzy queue FM/F(H1,H2)/1 with two classes, where arrival rate and service rates are all fuzzy numbers which are represented by triangular and trapezoidal fuzzy numbers. The basic idea is to obtain exact crisp values from the fuzzy value, which is more realistic in the practical queueing system. This is done by adopting left and right ranking method to remove the fuzziness before computing the performance measurements using conventional queueing theory. The main advantage of this approach is its simplicity in application, giving exact real data around fuzzy values. This approach can also be used in all types of queueing systems by taking two types of symmetrical linear membership functions. Numerical illustration is solved in this article to obtain two groups of crisp values in the queueing system under consideration.

  20. A practical engineering method for fuzzy reliability analysis of mechanical structures

    Energy Technology Data Exchange (ETDEWEB)

    Li Bing; Zhu Meilin; Xu Kai

    2000-03-01

    The fuzzy sets theory in reliability analyses is studied. The structure stress is related to several other variables, such as structure sizes, material properties, external loads; in most cases, it is difficult to be expressed in a mathematical formula, and the related variables are not random variables, but fuzzy variables or other uncertain variables which have not only randomness but also fuzziness. In this paper, a novel approach is presented to use the finite element analysis as a 'numerical experiment' tool, and to find directly, by fuzzy linear regression method, the statistical property of the structure stress. Based on the fuzzy stress-random strength interference model proposed in this paper, the fuzzy reliability of the mechanical structure can be evaluated. The compressor blade of a given turbocharger is then introduced as a realistic example to illustrate the approach.

  1. Fuzzy Filtering Method for Color Videos Corrupted by Additive Noise

    Directory of Open Access Journals (Sweden)

    Volodymyr I. Ponomaryov

    2014-01-01

    Full Text Available A novel method for the denoising of color videos corrupted by additive noise is presented in this paper. The proposed technique consists of three principal filtering steps: spatial, spatiotemporal, and spatial postprocessing. In contrast to other state-of-the-art algorithms, during the first spatial step, the eight gradient values in different directions for pixels located in the vicinity of a central pixel as well as the R, G, and B channel correlation between the analogous pixels in different color bands are taken into account. These gradient values give the information about the level of contamination then the designed fuzzy rules are used to preserve the image features (textures, edges, sharpness, chromatic properties, etc.. In the second step, two neighboring video frames are processed together. Possible local motions between neighboring frames are estimated using block matching procedure in eight directions to perform interframe filtering. In the final step, the edges and smoothed regions in a current frame are distinguished for final postprocessing filtering. Numerous simulation results confirm that this novel 3D fuzzy method performs better than other state-of-the-art techniques in terms of objective criteria (PSNR, MAE, NCD, and SSIM as well as subjective perception via the human vision system in the different color videos.

  2. Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering

    Directory of Open Access Journals (Sweden)

    Akara Sopharak

    2009-03-01

    Full Text Available Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM clustering method. The first result is then fine-tuned with morphological techniques. The detection results are validated by comparing with expert ophthalmologists’ hand-drawn ground-truths. Sensitivity, specificity, positive predictive value (PPV, positive likelihood ratio (PLR and accuracy are used to evaluate overall performance. It is found that the proposed method detects exudates successfully with sensitivity, specificity, PPV, PLR and accuracy of 87.28%, 99.24%, 42.77%, 224.26 and 99.11%, respectively.

  3. Second decision-making method of the fuzzy optimal model and its application

    Institute of Scientific and Technical Information of China (English)

    刘金禄; 陈守煜

    2004-01-01

    In the process of concept design offshore platforms, it is necessary to select the best from feasible alternatives through comparison and filtering. Fuzzy optimal selection in multiple stages is an useful method in engineering decisionmaking, but in some cases, it is difficult to make a distinction between the first alternative and the second. Therefore it is necessary to improve the fuzzy optimal selection method with multiple stages. In this paper, using fuzzy cross iteration optimal modeling, the weight sensitivity analysis is made firstly. Then a second decision-making method to solve fuzzy optimal problems is proposed. Its basic principle is the inverse reasoning in mathematics. This method expands the applied range of fuzzy optimal selection. Finally this method is used in the concept design of offshore platform. A case study shows that this method is scientific, reasonable and easy to use in practice.

  4. Numerical Solution of Fuzzy Differential Equations by Runge-Kutta Verner Method

    Directory of Open Access Journals (Sweden)

    T. Jayakumar

    2015-01-01

    Full Text Available In this paper we study the numerical methods for Fuzzy Differential equations by an application of the Runge-Kutta Verner method for fuzzy differential equations. We prove a convergence result and give numerical examples to illustrate the theory.

  5. A critical study of fuzzy logic as a scientific method in social sciences ...

    African Journals Online (AJOL)

    A critical study of fuzzy logic as a scientific method in social sciences. ... PROMOTING ACCESS TO AFRICAN RESEARCH ... The findings of this study show that Fuzzy logic doesn't have basic and necessary features of a scientific method and ...

  6. Method for solving fully fuzzy linear programming problems using deviation degree measure

    Institute of Scientific and Technical Information of China (English)

    Haifang Cheng; Weilai Huang; Jianhu Cai

    2013-01-01

    A new ful y fuzzy linear programming (FFLP) prob-lem with fuzzy equality constraints is discussed. Using deviation degree measures, the FFLP problem is transformed into a crispδ-parametric linear programming (LP) problem. Giving the value of deviation degree in each constraint, the δ-fuzzy optimal so-lution of the FFLP problem can be obtained by solving this LP problem. An algorithm is also proposed to find a balance-fuzzy optimal solution between two goals in conflict: to improve the va-lues of the objective function and to decrease the values of the deviation degrees. A numerical example is solved to il ustrate the proposed method.

  7. A Method Based on Intuitionistic Fuzzy Dependent Aggregation Operators for Supplier Selection

    Directory of Open Access Journals (Sweden)

    Fen Wang

    2013-01-01

    Full Text Available Recently, resolving the decision making problem of evaluation and ranking the potential suppliers have become as a key strategic factor for business firms. In this paper, two new intuitionistic fuzzy aggregation operators are developed: dependent intuitionistic fuzzy ordered weighed averaging (DIFOWA operator and dependent intuitionistic fuzzy hybrid weighed aggregation (DIFHWA operator. Some of their main properties are studied. A method based on the DIFHWA operator for intuitionistic fuzzy multiple attribute decision making is presented. Finally, an illustrative example concerning supplier selection is given.

  8. A Novel Approach for Shearer Memory Cutting Based on Fuzzy Optimization Method

    Directory of Open Access Journals (Sweden)

    Xin Zhou

    2013-01-01

    Full Text Available In order to improve the implement precision of shearer memory cutting, a novel approach based on the coal floor height variation which is taken as a significant factor and fuzzy optimization theory is proposed. The problem of shearer memory cutting is analyzed and the mathematic model is established. Moreover, the key technologies such as fuzzy control model, quantitative factors, and fuzzy control rules are elaborated, and the flowchart of shearer memory cutting method based on fuzzy optimization theory is designed. Finally, a simulation example is carried out and the proposed approach is proved feasible and efficient.

  9. Evaluation model of venture investment based on fuzzy matter-element method of combined weight

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    An evaluation model of an international venture investment project on the basis of fuzzy matter-element and combined weight methods is introduced. First, the compound fuzzy matter-element of optimal subordinate degree is constructed on the principle of the bigger-more-optimal or the less-more-optimal depending on the actual evaluation indicators, and combined with standard fuzzy matter-element to form a difference-square fuzzy matter-element. Secondly, a combined weight is calculated by both information ent...

  10. A FUZZY CLOPE ALGORITHM AND ITS OPTIMAL PARAMETER CHOICE

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Among the available clustering algorithms in data mining, the CLOPE algorithm attracts much more attention with its high speed and good performance. However, the proper choice of some parameters in the CLOPE algorithm directly affects the validity of the clustering results, which is still an open issue. For this purpose, this paper proposes a fuzzy CLOPE algorithm, and presents a method for the optimal parameter choice by defining a modified partition fuzzy degree as a clustering validity function. The experimental results with real data set illustrate the effectiveness of the proposed fuzzy CLOPE algorithm and optimal parameter choice method based on the modified partition fuzzy degree.

  11. Niching method using clustering crowding

    Institute of Scientific and Technical Information of China (English)

    GUO Guan-qi; GUI Wei-hua; WU Min; YU Shou-yi

    2005-01-01

    This study analyzes drift phenomena of deterministic crowding and probabilistic crowding by using equivalence class model and expectation proportion equations. It is proved that the replacement errors of deterministic crowding cause the population converging to a single individual, thus resulting in premature stagnation or losing optional optima. And probabilistic crowding can maintain equilibrium multiple subpopulations as the population size is adequate large. An improved niching method using clustering crowding is proposed. By analyzing topology of fitness landscape using hill valley function and extending the search space for similarity analysis, clustering crowding determines the locality of search space more accurately, thus greatly decreasing replacement errors of crowding. The integration of deterministic and probabilistic replacement increases the capacity of both parallel local hill climbing and maintaining multiple subpopulations. The experimental results optimizing various multimodal functions show that,the performances of clustering crowding, such as the number of effective peaks maintained, average peak ratio and global optimum ratio are uniformly superior to those of the evolutionary algorithms using fitness sharing, simple deterministic crowding and probabilistic crowding.

  12. A New Fuzzy Clustering-Ranking Algorithm and Its Application in Process Alarm Management%一种新的模糊聚类-分级算法及其在流程报警管理中的应用

    Institute of Scientific and Technical Information of China (English)

    朱群雄; 耿志强

    2005-01-01

    Overmany alarms of modern chemical process give the operators many difficulties to decision and diagnosis. In order to ensure safe production and process operating, management and optimization of alarm information are challenge work that must be confronted. A new process alarm management method based on fuzzy clusteringranking algorithm is proposed. The fuzzy clustering algorithm is used to cluster rationally the process variables,and difference driving decision algorithm ranks different clusters and process parameters in every cluster. The alarm signal of higher rank is handled preferentially to manage effectively alarms and avoid blind operation. The validity of proposed algorithm and solution is verified by the practical application of ethylene cracking furnace system. It is an effective and dependable alarm management method to improve operating safety in industrial process.

  13. Scoring methods used in cluster analysis

    OpenAIRE

    Sirota, Sergej

    2014-01-01

    The aim of the thesis is to compare methods of cluster analysis correctly classify objects in the dataset into groups, which are known. In the theoretical section first describes the steps needed to prepare a data file for cluster analysis. The next theoretical section is dedicated to the cluster analysis, which describes ways of measuring similarity of objects and clusters, and dedicated to description the methods of cluster analysis used in practical part of this thesis. In practical part a...

  14. Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering

    CERN Document Server

    Balakumaran, T; Shankar, C Gowri

    2010-01-01

    Microcalcifications in mammogram have been mainly targeted as a reliable earliest sign of breast cancer and their early detection is vital to improve its prognosis. Since their size is very small and may be easily overlooked by the examining radiologist, computer-based detection output can assist the radiologist to improve the diagnostic accuracy. In this paper, we have proposed an algorithm for detecting microcalcification in mammogram. The proposed microcalcification detection algorithm involves mammogram quality enhancement using multirresolution analysis based on the dyadic wavelet transform and microcalcification detection by fuzzy shell clustering. It may be possible to detect nodular components such as microcalcification accurately by introducing shape information. The effectiveness of the proposed algorithm for microcalcification detection is confirmed by experimental results.

  15. Control-Response Compatibility:Fuzzy Clustering Analysis and a Comparison Among Populations of Different Regions

    Institute of Scientific and Technical Information of China (English)

    于瑞峰; 王永县; 陈海寿; 彭海

    2004-01-01

    A group of 96 Mainland Chinese subjects were asked to respond to 12 questions by indicating their expectations about operation, direction-of-motion, and description of movement for items such as doors, keys, taps, and switches. Strong response pwere found for the whole questionnaire. Fuzzy clustering was used to analyze the structure and characteristics of Mainland Chinese stereotypes. The results for Mainland Chinese subjects were compared with those for Hong Kong Chinese and Americans reported earlier. There are no significant differences for the population stereotypes for daily operational tasks in the three regions. The responses of the Hong Kong Chinese and Mainland Chinese are similar, but with significant variations between different populations, especially for some specific items in the questionnaire.

  16. Fusion Method for Remote Sensing Image Based on Fuzzy Integral

    Directory of Open Access Journals (Sweden)

    Hui Zhou

    2014-01-01

    Full Text Available This paper presents a kind of image fusion method based on fuzzy integral, integrated spectral information, and 2 single factor indexes of spatial resolution in order to greatly retain spectral information and spatial resolution information in fusion of multispectral and high-resolution remote sensing images. Firstly, wavelet decomposition is carried out to two images, respectively, to obtain wavelet decomposition coefficients of the two image and keep coefficient of low frequency of multispectral image, and then optimized fusion is carried out to high frequency part of the two images based on weighting coefficient to generate new fusion image. Finally, evaluation is carried out to the image after fusion with introduction of evaluation indexes of correlation coefficient, mean value of image, standard deviation, distortion degree, information entropy, and so forth. The test results show that this method integrated multispectral information and space high-resolution information in a better way, and it is an effective fusion method of remote sensing image.

  17. Controlling Smart Green House Using Fuzzy Logic Method

    Directory of Open Access Journals (Sweden)

    Rafiuddin Syam

    2015-10-01

    Full Text Available To increase agricultural output it is needed a system that can help the environmental conditions for optimum plant growth. Smart greenhouse allows for plants to grow optimally, because the temperature and humidity can be controlled so that no drastic changes. It is necessary for optimal smart greenhouse needed a system to manipulate the environment in accordance with the needs of the plant. In this case the setting temperature and humidity in the greenhouse according to the needs of the plant. So using an automated system for keeping such environmental condition is important. In this study, the authors use fuzzy logic to make the duration of watering the plants more dynamic in accordance with the input temperature and humidity so that the temperature and humidity in the green house plants maintained in accordance to the reference condition. Based on the experimental results using fuzzy logic method is effective to control the duration of watering and to maintain the optimum temperature and humidity inside the greenhouse

  18. Applications of Fuzzy Set Theory to Satellite Soundings

    Science.gov (United States)

    Munteanu, M. J.

    1985-01-01

    The introduction of an appropriate fuzzy setting for satellite soundings and its application to clustering methods via unimodal fuzzy sets in the future is proposed. Methods of hard clustering analysis and fuzzy partitioned clustering were applied on simulated data with very encouraging results. The proposed clustering technique is discussed. The notion of a unimodal fuzzy set was chosen to represent the partition of a data set for two reasons: (1) it detects all the locations in the vector space where highly concentrated clusters of points exist; and (2) the notion is general enough to represent clusters that exhibit quite general distributions of points. The technique detects all of the existing unimodal fuzzy sets and realizes the maximum separation among them. It is economical in memory space and computational time requirements and also detects groups that are fairly generally distributed in the feature space.

  19. A Novel Method for Multiattribute Decision Making with Dual Hesitant Fuzzy Triangular Linguistic Information

    Directory of Open Access Journals (Sweden)

    Yanbing Ju

    2014-01-01

    Full Text Available This paper studies the multiattribute decision making (MADM problems in which the attribute values take the form of dual hesitant fuzzy triangular linguistic elements and the weights of attributes take the form of real numbers. Firstly, to solve the situation where the membership degree and the nonmembership degree of an element to a triangular linguistic variable, the concept, operational laws, score function, and accuracy function of dual hesitant fuzzy triangular linguistic elements (DHFTLEs are defined. Then, some dual hesitant fuzzy triangular linguistic geometric aggregation operators are developed for aggregating the DHFTLEs, including dual hesitant fuzzy triangular linguistic weighted geometric (DHFTLWG operator, dual hesitant fuzzy triangular linguistic ordered weighted geometric (DHFTLOWG operator, dual hesitant fuzzy triangular linguistic hybrid geometric (DHFTLHG operator, generalized dual hesitant fuzzy triangular linguistic weighted geometric (GDHFTLWG operator, and generalized dual hesitant fuzzy triangular linguistic ordered weighted geometric (GDHFTLOWG operator. Furthermore, some desirable properties of these operators are investigated in detail. Based on the proposed operators, an approach to MADM with dual hesitant fuzzy triangular linguistic information is proposed. Finally, a numerical example for investment alternative selection is given to illustrate the application of the proposed method.

  20. Effect of co-operative fuzzy c-means clustering on estimates of three parameters AVA inversion

    Indian Academy of Sciences (India)

    Rajesh R Nair; Suresh Ch Kandpal

    2010-04-01

    We determine the degree of variation of model fitness,to a true model based on amplitude variation with angle (AVA)methodology for a synthetic gas hydrate model,using co-operative fuzzy c-means clustering,constrained to a rock physics model.When a homogeneous starting model is used,with only traditional least squares optimization scheme for inversion,the variance of the parameters is found to be comparatively high.In this co-operative methodology,the output from the least squares inversion is fed as an input to the fuzzy scheme.Tests with co-operative inversion using fuzzy c-means with damped least squares technique and constraints derived from empirical relationship based on rock properties model show improved stability,model fitness and variance for all the three parameters in comparison with the standard inversion alone.

  1. APPLYING ROBUST RANKING METHOD IN TWO PHASE FUZZY OPTIMIZATION LINEAR PROGRAMMING PROBLEMS (FOLPP

    Directory of Open Access Journals (Sweden)

    Monalisha Pattnaik

    2014-12-01

    Full Text Available Background: This paper explores the solutions to the fuzzy optimization linear program problems (FOLPP where some parameters are fuzzy numbers. In practice, there are many problems in which all decision parameters are fuzzy numbers, and such problems are usually solved by either probabilistic programming or multi-objective programming methods. Methods: In this paper, using the concept of comparison of fuzzy numbers, a very effective method is introduced for solving these problems. This paper extends linear programming based problem in fuzzy environment. With the problem assumptions, the optimal solution can still be theoretically solved using the two phase simplex based method in fuzzy environment. To handle the fuzzy decision variables can be initially generated and then solved and improved sequentially using the fuzzy decision approach by introducing robust ranking technique. Results and conclusions: The model is illustrated with an application and a post optimal analysis approach is obtained. The proposed procedure was programmed with MATLAB (R2009a version software for plotting the four dimensional slice diagram to the application. Finally, numerical example is presented to illustrate the effectiveness of the theoretical results, and to gain additional managerial insights. 

  2. A Novel Method for Optimal Solution of Fuzzy Chance Constraint Single-Period Inventory Model

    Directory of Open Access Journals (Sweden)

    Anuradha Sahoo

    2016-01-01

    Full Text Available A method is proposed for solving single-period inventory fuzzy probabilistic model (SPIFPM with fuzzy demand and fuzzy storage space under a chance constraint. Our objective is to maximize the total profit for both overstock and understock situations, where the demand D~j for each product j in the objective function is considered as a fuzzy random variable (FRV and with the available storage space area W~, which is also a FRV under normal distribution and exponential distribution. Initially we used the weighted sum method to consider both overstock and understock situations. Then the fuzziness of the model is removed by ranking function method and the randomness of the model is removed by chance constrained programming problem, which is a deterministic nonlinear programming problem (NLPP model. Finally this NLPP is solved by using LINGO software. To validate and to demonstrate the results of the proposed model, numerical examples are given.

  3. Application of an iterative method and an evolutionary algorithm in fuzzy optimization

    Directory of Open Access Journals (Sweden)

    Ricardo Coelho Silva

    2012-08-01

    Full Text Available This work develops two approaches based on the fuzzy set theory to solve a class of fuzzy mathematical optimization problems with uncertainties in the objective function and in the set of constraints. The first approach is an adaptation of an iterative method that obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. The second one is a metaheuristic approach that adapts a standard genetic algorithm to use fuzzy numbers. Both approaches use a decision criterion called satisfaction level that reaches the best solution in the uncertain environment. Selected examples from the literature are presented to compare and to validate the efficiency of the methods addressed, emphasizing the fuzzy optimization problem in some import-export companies in the south of Spain.

  4. A novel fuzzy set based multifactor dimensionality reduction method for detecting gene-gene interaction.

    Science.gov (United States)

    Jung, Hye-Young; Leem, Sangseob; Lee, Sungyoung; Park, Taesung

    2016-12-01

    Gene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. In order to identify the best interaction model associated with disease susceptibility, MDR compares all possible genotype combinations in terms of their predictability of disease status from a simple binary high(H) and low(L) risk classification. However, this simple binary classification does not reflect the uncertainty of H/L classification. We regard classifying H/L as equivalent to defining the degree of membership of two risk groups H/L. By adopting the fuzzy set theory, we propose Fuzzy MDR which takes into account the uncertainty of H/L classification. Fuzzy MDR allows the possibility of partial membership of H/L through a membership function which transforms the degree of uncertainty into a [0,1] scale. The best genotype combinations can be selected which maximizes a new fuzzy set based accuracy measure. Two simulation studies are conducted to compare the power of the proposed Fuzzy MDR with that of MDR. Our results show that Fuzzy MDR has higher power than MDR. We illustrate the proposed Fuzzy MDR by analysing bipolar disorder (BD) trait of the WTCCC dataset to detect GGI associated with BD. We propose a novel Fuzzy MDR method to detect gene-gene interaction by taking into account the uncertainly of H/L classification and show that it has higher power than MDR. Fuzzy MDR can be easily extended to handle continuous phenotypes as well. The program written in R for the proposed Fuzzy MDR is available at https://statgen.snu.ac.kr/software/FuzzyMDR. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Using a fuzzy comprehensive evaluation method to determine product usability: A test case.

    Science.gov (United States)

    Zhou, Ronggang; Chan, Alan H S

    2017-01-01

    In order to take into account the inherent uncertainties during product usability evaluation, Zhou and Chan [1] proposed a comprehensive method of usability evaluation for products by combining the analytic hierarchy process (AHP) and fuzzy evaluation methods for synthesizing performance data and subjective response data. This method was designed to provide an integrated framework combining the inevitable vague judgments from the multiple stages of the product evaluation process. In order to illustrate the effectiveness of the model, this study used a summative usability test case to assess the application and strength of the general fuzzy usability framework. To test the proposed fuzzy usability evaluation framework [1], a standard summative usability test was conducted to benchmark the overall usability of a specific network management software. Based on the test data, the fuzzy method was applied to incorporate both the usability scores and uncertainties involved in the multiple components of the evaluation. Then, with Monte Carlo simulation procedures, confidence intervals were used to compare the reliabilities among the fuzzy approach and two typical conventional methods combining metrics based on percentages. This case study showed that the fuzzy evaluation technique can be applied successfully for combining summative usability testing data to achieve an overall usability quality for the network software evaluated. Greater differences of confidence interval widths between the method of averaging equally percentage and weighted evaluation method, including the method of weighted percentage averages, verified the strength of the fuzzy method.

  6. Transformation and entropy for fuzzy rough sets

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    A new method for translating a fuzzy rough set to a fuzzy set is introduced and the fuzzy approximation of a fuzzy rough set is given.The properties of the fuzzy approximation of a fuzzy rough set are studied and a fuzzy entropy measure for fuzzy rough sets is proposed.This measure is consistent with similar considerations for ordinary fuzzy sets and is the result of the fuzzy approximation of fuzzy rough sets.

  7. The SMART CLUSTER METHOD - adaptive earthquake cluster analysis and declustering

    Science.gov (United States)

    Schaefer, Andreas; Daniell, James; Wenzel, Friedemann

    2016-04-01

    Earthquake declustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity with usual applications comprising of probabilistic seismic hazard assessments (PSHAs) and earthquake prediction methods. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation. Various methods have been developed to address this issue from other researchers. These have differing ranges of complexity ranging from rather simple statistical window methods to complex epidemic models. This study introduces the smart cluster method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal identification. Hereby, an adaptive search algorithm for data point clusters is adopted. It uses the earthquake density in the spatio-temporal neighbourhood of each event to adjust the search properties. The identified clusters are subsequently analysed to determine directional anisotropy, focussing on a strong correlation along the rupture plane and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010/2011 Darfield-Christchurch events, an adaptive classification procedure is applied to disassemble subsequent ruptures which may have been grouped into an individual cluster using near-field searches, support vector machines and temporal splitting. The steering parameters of the search behaviour are linked to local earthquake properties like magnitude of completeness, earthquake density and Gutenberg-Richter parameters. The method is capable of identifying and classifying earthquake clusters in space and time. It is tested and validated using earthquake data from California and New Zealand. As a result of the cluster identification process, each event in

  8. Development of the method of landslide hazard assessment on areas of highways in terms of fuzzy information

    Directory of Open Access Journals (Sweden)

    Леонід Іванович Нефьодов

    2014-08-01

    Full Text Available Factors of landslide hazard on areas of highways have been analyzed. The main steps of fuzzy inference method are described. The method of landslide hazard assessment on areas of highways in terms of fuzzy information has been developed. The example of landslide hazard assessment on areas of highways in terms of fuzzy information in Matlab has been provided.

  9. [Selectivity rank regionalization of Paeonia lactiflora based on fuzzy method].

    Science.gov (United States)

    Lv, Jinrong; Guo, Lanping; Huang, Luqi; Liang, Liuke; Sun, Yuzhang; Zhang, Xiaobo; Han, Xiaoli; Zhang, Hongjun

    2009-04-01

    For optimal adaptive cultivation region selection, we used ecology factors characterized Duolun region as model area to carry out the adaptive habitat division of Paeonia lactiflora. Similar priority comparison of ecology factors.in 91 cities were calculated by Fuzzy methods, then, distance of the ecology factors were transferred to spacial model by geography information system (,GIS) and modified by soil utilization map of China. The results showed that P. lactiflora were mainly distributed in the Daxing'an Mountain, Changbaishan and qinling range which were divided into six grades of suitable regions belonging to three geographical distributed units. The most similar areas to Duolun were Huade, Xilinhaote, Suolun and Zhangbei. P. lactiflora's distribution and quality are relevant with longitude and latitude, and temperature and rainfall.

  10. Clustering of hydrological data: a review of methods for runoff predictions in ungauged basins

    Science.gov (United States)

    Dogulu, Nilay; Kentel, Elcin

    2017-04-01

    There is a great body of research that has looked into the challenge of hydrological predictions in ungauged basins as driven by the Prediction in Ungauged Basins (PUB) initiative of the International Association of Hydrological Sciences (IAHS). Transfer of hydrological information (e.g. model parameters, flow signatures) from gauged to ungauged catchment, often referred as "regionalization", is the main objective and benefits from identification of hydrologically homogenous regions. Within this context, indirect representation of hydrologic similarity for ungauged catchments, which is not a straightforward task due to absence of streamflow measurements and insufficient knowledge of hydrologic behavior, has been explored in the literature. To this aim, clustering methods have been widely adopted. While most of the studies employ hard clustering techniques such as hierarchical (divisive or agglomerative) clustering, there have been more recent attempts taking advantage of fuzzy set theory (fuzzy clustering) and nonlinear methods (e.g. self-organizing maps). The relevant research findings from this fundamental task of hydrologic sciences have revealed the value of different clustering methods for improved understanding of catchment hydrology. However, despite advancements there still remains challenges and yet opportunities for research on clustering for regionalization purposes. The present work provides an overview of clustering techniques and their applications in hydrology with focus on regionalization for the PUB problem. Identifying their advantages and disadvantages, we discuss the potential of innovative clustering methods and reflect on future challenges in view of the research objectives of the PUB initiative.

  11. Identifying Unique Neighborhood Characteristics to Guide Health Planning for Stroke and Heart Attack: Fuzzy Cluster and Discriminant Analyses Approaches

    Science.gov (United States)

    Pedigo, Ashley; Seaver, William; Odoi, Agricola

    2011-01-01

    Background Socioeconomic, demographic, and geographic factors are known determinants of stroke and myocardial infarction (MI) risk. Clustering of these factors in neighborhoods needs to be taken into consideration during planning, prioritization and implementation of health programs intended to reduce disparities. Given the complex and multidimensional nature of these factors, multivariate methods are needed to identify neighborhood clusters of these determinants so as to better understand the unique neighborhood profiles. This information is critical for evidence-based health planning and service provision. Therefore, this study used a robust multivariate approach to classify neighborhoods and identify their socio-demographic characteristics so as to provide information for evidence-based neighborhood health planning for stroke and MI. Methods and Findings The study was performed in East Tennessee Appalachia, an area with one of the highest stroke and MI risks in USA. Robust principal component analysis was performed on neighborhood (census tract) socioeconomic and demographic characteristics, obtained from the US Census, to reduce the dimensionality and influence of outliers in the data. Fuzzy cluster analysis was used to classify neighborhoods into Peer Neighborhoods (PNs) based on their socioeconomic and demographic characteristics. Nearest neighbor discriminant analysis and decision trees were used to validate PNs and determine the characteristics important for discrimination. Stroke and MI mortality risks were compared across PNs. Four distinct PNs were identified and their unique characteristics and potential health needs described. The highest risk of stroke and MI mortality tended to occur in less affluent PNs located in urban areas, while the suburban most affluent PNs had the lowest risk. Conclusions Implementation of this multivariate strategy provides health planners useful information to better understand and effectively plan for the unique

  12. Convex Decomposition Based Cluster Labeling Method for Support Vector Clustering

    Institute of Scientific and Technical Information of China (English)

    Yuan Ping; Ying-Jie Tian; Ya-Jian Zhou; Yi-Xian Yang

    2012-01-01

    Support vector clustering (SVC) is an important boundary-based clustering algorithm in multiple applications for its capability of handling arbitrary cluster shapes. However,SVC's popularity is degraded by its highly intensive time complexity and poor label performance.To overcome such problems,we present a novel efficient and robust convex decomposition based cluster labeling (CDCL) method based on the topological property of dataset.The CDCL decomposes the implicit cluster into convex hulls and each one is comprised by a subset of support vectors (SVs).According to a robust algorithm applied in the nearest neighboring convex hulls,the adjacency matrix of convex hulls is built up for finding the connected components; and the remaining data points would be assigned the label of the nearest convex hull appropriately.The approach's validation is guaranteed by geometric proofs.Time complexity analysis and comparative experiments suggest that CDCL improves both the efficiency and clustering quality significantly.

  13. A Modern Syllogistic Method in Intuitionistic Fuzzy Logic with Realistic Tautology

    Directory of Open Access Journals (Sweden)

    Ali Muhammad Rushdi

    2015-01-01

    Full Text Available The Modern Syllogistic Method (MSM of propositional logic ferrets out from a set of premises all that can be concluded from it in the most compact form. The MSM combines the premises into a single function equated to 1 and then produces the complete product of this function. Two fuzzy versions of MSM are developed in Ordinary Fuzzy Logic (OFL and in Intuitionistic Fuzzy Logic (IFL with these logics augmented by the concept of Realistic Fuzzy Tautology (RFT which is a variable whose truth exceeds 0.5. The paper formally proves each of the steps needed in the conversion of the ordinary MSM into a fuzzy one. The proofs rely mainly on the successful replacement of logic 1 (or ordinary tautology by an RFT. An improved version of Blake-Tison algorithm for generating the complete product of a logical function is also presented and shown to be applicable to both crisp and fuzzy versions of the MSM. The fuzzy MSM methodology is illustrated by three specific examples, which delineate differences with the crisp MSM, address the question of validity values of consequences, tackle the problem of inconsistency when it arises, and demonstrate the utility of the concept of Realistic Fuzzy Tautology.

  14. Risk Evaluation Approach and Application Research on Fuzzy-FMECA Method Based on Cloud Model

    Directory of Open Access Journals (Sweden)

    Zhengjie Xu

    2013-09-01

    Full Text Available In order to safeguard the safety of passengers and reducemaintenance costs, it is necessary to analyze and evaluate the security risk ofthe Railway Signal System. However, the conventional Fuzzy Analytical HierarchyProcess (FAHP can not describe the fuzziness and randomness of the judgment,accurately, and once the fuzzy sets are described using subjection degreefunction, the concept of fuzziness will be no longer fuzzy. Thus Fuzzy-FMECAmethod based on cloud model is put forward. Failure Modes Effects andCriticality Analysis (FMECA method is used to identify the risk and FAHP basedon cloud model is used for determining the subjection degree function in fuzzymethod, finally the group decision can be gained with the syntheticallyaggregated cloud model, the method’s feasibility and effectiveness are shown inthe practical examples. Finally Fuzzy-FMECA based on cloud model and theconventional FAHP are used to assess the risk respectively, evaluation resultsshow that the cloud model which is introduced into the risk assessment ofRailway Signal System can realize the transition between precise value andquality value by combining the fuzziness and randomness and provide moreabundant information than subjection degree function of the conventional FAHP.

  15. A Modern Syllogistic Method in Intuitionistic Fuzzy Logic with Realistic Tautology.

    Science.gov (United States)

    Rushdi, Ali Muhammad; Zarouan, Mohamed; Alshehri, Taleb Mansour; Rushdi, Muhammad Ali

    2015-01-01

    The Modern Syllogistic Method (MSM) of propositional logic ferrets out from a set of premises all that can be concluded from it in the most compact form. The MSM combines the premises into a single function equated to 1 and then produces the complete product of this function. Two fuzzy versions of MSM are developed in Ordinary Fuzzy Logic (OFL) and in Intuitionistic Fuzzy Logic (IFL) with these logics augmented by the concept of Realistic Fuzzy Tautology (RFT) which is a variable whose truth exceeds 0.5. The paper formally proves each of the steps needed in the conversion of the ordinary MSM into a fuzzy one. The proofs rely mainly on the successful replacement of logic 1 (or ordinary tautology) by an RFT. An improved version of Blake-Tison algorithm for generating the complete product of a logical function is also presented and shown to be applicable to both crisp and fuzzy versions of the MSM. The fuzzy MSM methodology is illustrated by three specific examples, which delineate differences with the crisp MSM, address the question of validity values of consequences, tackle the problem of inconsistency when it arises, and demonstrate the utility of the concept of Realistic Fuzzy Tautology.

  16. The application of fuzzy-based methods to central nerve fiber imaging

    DEFF Research Database (Denmark)

    Axer, Hubertus; Jantzen, Jan; Keyserlingk, Diedrich Graf v.;

    2003-01-01

    This paper discusses the potential of fuzzy logic methods within medical imaging. Technical advances have produced imaging techniques that can visualize structures and their functions in the living human body. The interpretation of these images plays a prominent role in diagnostic and therapeutic....... Fuzzy logic methods were applied to analyze these pictures from low- to high-level image processing. The solutions presented here are motivated by problems of routine neuroanatomic research demonstrating fuzzy-based methods to be valuable tools in medical image processing....

  17. Fuzzy Clustering Applied to ROI Detection in Helical Thoracic CT Scans with a New Proposal and Variants.

    Science.gov (United States)

    Castro, Alfonso; Rey, Alberto; Boveda, Carmen; Arcay, Bernardino; Sanjurjo, Pedro

    2016-01-01

    The detection of pulmonary nodules is one of the most studied problems in the field of medical image analysis due to the great difficulty in the early detection of such nodules and their social impact. The traditional approach involves the development of a multistage CAD system capable of informing the radiologist of the presence or absence of nodules. One stage in such systems is the detection of ROI (regions of interest) that may be nodules in order to reduce the space of the problem. This paper evaluates fuzzy clustering algorithms that employ different classification strategies to achieve this goal. After characterising these algorithms, the authors propose a new algorithm and different variations to improve the results obtained initially. Finally it is shown as the most recent developments in fuzzy clustering are able to detect regions that may be nodules in CT studies. The algorithms were evaluated using helical thoracic CT scans obtained from the database of the LIDC (Lung Image Database Consortium).

  18. Anchor person shot detection for news video indexing based on graph-theoretical clustering and fuzzy if-then rules

    Science.gov (United States)

    Gao, Xinbo; Li, Qi; Li, Jie

    2003-09-01

    Anchorperson shot detection is of significance for video shot semantic parsing and indexing clues extraction in content-based news video indexing and retrieval system. This paper presents a model-free anchorperson shot detection scheme based on the graph-theoretical clustering and fuzzy interference. First, a news video is segmented into video shots with any an effective video syntactic parsing algorithm. For each shot, one frame is extracted from the frame sequence as a representative key frame. Then the graph-theoretical clustering algorithm is performed on the key frames to identify the anchorperson frames. The anchorperson frames are further refined based on face detection and fuzzy interference with if-then rules. The proposed scheme achieves a precision of 98.40% and a recall of over 97.69% in the anchorperson shot detection experiment.

  19. An Overview on Clustering Methods

    CERN Document Server

    Madhulatha, T Soni

    2012-01-01

    Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets, so that the data in each subset according to some defined distance measure. This paper covers about clustering algorithms, benefits and its applications. Paper concludes by discussing some limitations.

  20. Multi-label classification based on semi-fuzzy kernel clustering and fuzzy support vector machine%基于模糊支持向量的多标签分类方法

    Institute of Scientific and Technical Information of China (English)

    郑文博; 杨燕; 王洪军

    2011-01-01

    单实例多标签分类是指一个样本拥有多个标签的分类问题,对此提出了一种基于半模糊核聚类和模糊支持向量机的多标签分类算法.该算法采用一对一分解策略将多类多标签数据集分解为多个两类双标签数据子集,在每个子集上训练两类双标签模糊支持向量机.为提高分类器的性能引入了半模糊核聚类技术.实验结果表明,与现有的一些算法相比新算法具有其优越性.%Single instance multi-label classification problem lies in that its sample may own multiple classes. Aiming at this subject a multi-label classification algorithm based on fuzzy support vector machine (FSVM) and semi-fuzzy kernel clustering is proposed. One versus one decomposition policy is used to decompose the multi-label problem into several binary class double label classification sub-problems. For each sub-problem, a sub-classifier using binary class double label FSVM model is built. To improve the classification performance, a kind of semi-fuzzy kernel clustering technology is employed. Experimental results show that the proposed method is superior to several existent multi-label classification algorithms.

  1. Improved method for the feature extraction of laser scanner using genetic clustering

    Institute of Scientific and Technical Information of China (English)

    Yu Jinxia; Cai Zixing; Duan Zhuohua

    2008-01-01

    Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method based on genetic clustering VGA-clustering is presented. By integrating the spatial neighbouring information of range data into fuzzy clustering algorithm, a weighted fuzzy clustering algorithm (WFCA) instead of standard clustering algorithm is introduced to realize feature extraction of laser scanner. Aimed at the unknown clustering number in advance, several validation index functions are used to estimate the validity of different clustering al-gorithms and one validation index is selected as the fitness function of genetic algorithm so as to determine the accurate clustering number automatically. At the same time, an improved genetic algorithm IVGA on the basis of VGA is proposed to solve the local optimum of clustering algorithm, which is implemented by increasing the population diversity and improving the genetic operators of elitist rule to enhance the local search capacity and to quicken the convergence speed. By the comparison with other algorithms, the effectiveness of the algorithm introduced is demonstrated.

  2. Water quality evaluation based on improved fuzzy matter-element method

    Institute of Scientific and Technical Information of China (English)

    Dongjun Liu; Zhihong Zou

    2012-01-01

    For natural water,method of water quality evaluation based on improved fuzzy matter-element evaluation method is presented.Two important parts are improved,the weights determining and fuzzy membership functions.The coefficient of variation of each indicator is used to determine the weight instead of traditional calculating superscales method.On the other hand,fuzzy matter-elements are constructed,and normal membership degrees are used instead of traditional trapezoidal ones.The composite fuzzy matter-elements with associated coefficient are constructed through associated transformation.The levels of natural water quality are determined according to the principle of maximum correlation.The improved fuzzy matter-element evaluation method is applied to evaluate water quality of the Luokou mainstream estuary at the first ten weeks in 2011 with the coefficient of variation method determining the weights.Water quality of Luokou mainstream estuary is dropping from level Ⅰ to level Ⅱ.The results of the improved evaluation method are basically the same as the official water quality.The variation coefficient method can reduce the workload,and overcome the adverse effects from abnormal values,compared with the traditional calculating superscales method.The results of improved fuzzy matterelement evaluation method are more credible than the ones of the traditional evaluation method.The improved evaluation method can use information of monitoring data more scientifically and comprehensively,and broaden a new evaluation method for water quality assessment.

  3. RCWIM - an improved global water isotope pattern prediction model using fuzzy climatic clustering regionalization

    Science.gov (United States)

    Terzer, Stefan; Araguás-Araguás, Luis; Wassenaar, Leonard I.; Aggarwal, Pradeep K.

    2013-04-01

    Prediction of geospatial H and O isotopic patterns in precipitation has become increasingly important to diverse disciplines beyond hydrology, such as climatology, ecology, food authenticity, and criminal forensics, because these two isotopes of rainwater often control the terrestrial isotopic spatial patterns that facilitate the linkage of products (food, wildlife, water) to origin or movement (food, criminalistics). Currently, spatial water isotopic pattern prediction relies on combined regression and interpolation techniques to create gridded datasets by using data obtained from the Global Network of Isotopes In Precipitation (GNIP). However, current models suffer from two shortcomings: (a) models may have limited covariates and/or parameterization fitted to a global domain, which results in poor predictive outcomes at regional scales, or (b) the spatial domain is intentionally restricted to regional settings, and thereby of little use in providing information at global geospatial scales. Here we present a new global climatically regionalized isotope prediction model which overcomes these limitations through the use of fuzzy clustering of climatic data subsets, allowing us to better identify and customize appropriate covariates and their multiple regression coefficients instead of aiming for a one-size-fits-all global fit (RCWIM - Regionalized Climate Cluster Water Isotope Model). The new model significantly reduces the point-based regression residuals and results in much lower overall isotopic prediction uncertainty, since residuals are interpolated onto the regression surface. The new precipitation δ2H and δ18O isoscape model is available on a global scale at 10 arc-minutes spatial and at monthly, seasonal and annual temporal resolution, and will provide improved predicted stable isotope values used for a growing number of applications. The model further provides a flexible framework for future improvements using regional climatic clustering.

  4. EFFECT OF CLUSTERING IN DESIGNING A FUZZY BASED HYBRID INTRUSION DETECTION SYSTEM FOR MOBILE AD HOC NETWORKS

    Directory of Open Access Journals (Sweden)

    D. Vydeki

    2013-01-01

    Full Text Available Intrusion Detection System (IDS provides additional security for the most vulnerable Mobile Adhoc Networks (MANET. Use of Fuzzy Inference System (FIS in the design of IDS is proven to be efficient in detecting routing attacks in MANETs. Clustering is a vital means in the detection process of FIS based hybrid IDS. This study describes the design of such a system to detect black hole attack in MANET that uses Adhoc On-Demand Distance Vector (AODV routing protocol. It analyses the effect of two clustering algorithms and also prescribes the suitable clustering algorithm for the above-mentioned IDS. MANETs with various traffic scenarios were simulated and the data set required for the IDS is extracted. A hybrid IDS is designed using Sugeno type-2 FIS to detect black hole attack. From the experimental results, it is derived that the subtractive clustering algorithm produces 97% efficient detection while FCM offers 91%. It has been found that the subtractive clustering algorithm is more fit and efficient than the Fuzzy C-Means clustering (FCM for the FIS based detection system.

  5. FUZZY ECCENTRICITY AND GROSS ERROR IDENTIFICATION

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The dominant and recessive effect made by exceptional interferer is analyzed in measurement system based on responsive character, and the gross error model of fuzzy clustering based on fuzzy relation and fuzzy equipollence relation is built. The concept and calculate formula of fuzzy eccentricity are defined to deduce the evaluation rule and function of gross error, on the base of them, a fuzzy clustering method of separating and discriminating the gross error is found. Utilized in the dynamic circular division measurement system, the method can identify and eliminate gross error in measured data, and reduce measured data dispersity. Experimental results indicate that the use of the method and model enables repetitive precision of the system to improve 80% higher than the foregoing system, to reach 3.5 s, and angle measurement error is less than 7 s.

  6. A Continuous Clustering Method for Vector Fields

    NARCIS (Netherlands)

    Garcke, H.; Preußer, T.; Rumpf, M.; Telea, A.; Weikard, U.; Wijk, J. van

    2000-01-01

    A new method for the simplification of flow fields is presented. It is based on continuous clustering. A well-known physical clustering model, the Cahn Hillard model which describes phase separation, is modified to reflect the properties of the data to be visualized. Clusters are defined implicitly

  7. A novel computing three-dimensional differential transform method for solving fuzzy partial differential equations

    OpenAIRE

    Farshid Mirzaee; Mohammad Komak Yari

    2016-01-01

    In this paper, we introduce three-dimensional fuzzy differential transform method and we utilize it to solve fuzzy partial differential equations. This technique is a successful method because of reducing such problems to solve a system of algebraic equations; so, the problem can be solved directly. A considerable advantage of this method is to obtain the analytical solutions if the equation has an exact solution that is a polynomial function. Numerical examples are included to demonstrate th...

  8. Fuzzy Comprehensive Assessment Method to Determine Tectonic Stress Patterns

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hai; QI Lan; HAO Caizhe; GUO Lei

    2007-01-01

    The tectonic stress patterns were determined by a fuzzy comprehensive assessment method. Data of in-situ survey and fault information were utilized in the method. First, by making pressure and tension in the directions of along-river, cross-river, shear clockwise, and shear counter-clockwise, 26 types of tectonic stress patterns were presented. And the stress vector of each pat-tern was obtained with FE software by taking unit displacement as boundary load. Then, by takingthe 26 types of tectonic stress patterns as index set and 3 main stresses as factor set and choosing various operators, comparison of directions of computational stress vector and survey stress vector was made and the most possible tectonic stress pattern was obtained. Taking the 26 types of tectonic stress patterns as index set and strike angle as factor set, comparison of relationships between formation of fault and tectonic stress was made, and the tectonic stress patterns were assessed with known fault information. By summarizing the above assessment results, the most impossible tectonic stress pattern was obtained. Finally an engineering case was quoted to validate that the method is more feasible and reliable than traditional empirical method.

  9. Interior Point Method for Solving Fuzzy Number Linear Programming Problems Using Linear Ranking Function

    Directory of Open Access Journals (Sweden)

    Yi-hua Zhong

    2013-01-01

    Full Text Available Recently, various methods have been developed for solving linear programming problems with fuzzy number, such as simplex method and dual simplex method. But their computational complexities are exponential, which is not satisfactory for solving large-scale fuzzy linear programming problems, especially in the engineering field. A new method which can solve large-scale fuzzy number linear programming problems is presented in this paper, which is named a revised interior point method. Its idea is similar to that of interior point method used for solving linear programming problems in crisp environment before, but its feasible direction and step size are chosen by using trapezoidal fuzzy numbers, linear ranking function, fuzzy vector, and their operations, and its end condition is involved in linear ranking function. Their correctness and rationality are proved. Moreover, choice of the initial interior point and some factors influencing the results of this method are also discussed and analyzed. The result of algorithm analysis and example study that shows proper safety factor parameter, accuracy parameter, and initial interior point of this method may reduce iterations and they can be selected easily according to the actual needs. Finally, the method proposed in this paper is an alternative method for solving fuzzy number linear programming problems.

  10. Group decision-making approach for flood vulnerability identification using the fuzzy VIKOR method

    Science.gov (United States)

    Lee, G.; Jun, K. S.; Chung, E.-S.

    2015-04-01

    This study proposes an improved group decision making (GDM) framework that combines the VIKOR method with data fuzzification to quantify the spatial flood vulnerability including multiple criteria. In general, GDM method is an effective tool for formulating a compromise solution that involves various decision makers since various stakeholders may have different perspectives on their flood risk/vulnerability management responses. The GDM approach is designed to achieve consensus building that reflects the viewpoints of each participant. The fuzzy VIKOR method was developed to solve multi-criteria decision making (MCDM) problems with conflicting and noncommensurable criteria. This comprising method can be used to obtain a nearly ideal solution according to all established criteria. This approach effectively can propose some compromising decisions by combining the GDM method and fuzzy VIKOR method. The spatial flood vulnerability of the southern Han River using the GDM approach combined with the fuzzy VIKOR method was compared with the spatial flood vulnerability using general MCDM methods, such as the fuzzy TOPSIS and classical GDM methods (i.e., Borda, Condorcet, and Copeland). As a result, the proposed fuzzy GDM approach can reduce the uncertainty in the data confidence and weight derivation techniques. Thus, the combination of the GDM approach with the fuzzy VIKOR method can provide robust prioritization because it actively reflects the opinions of various groups and considers uncertainty in the input data.

  11. Combined indirect and direct method for adaptive fuzzy output feedback control of nonlinear system

    Institute of Scientific and Technical Information of China (English)

    Ding Quanxin; Chen Haitong; Jiang Changsheng; Chen Zongji

    2007-01-01

    A novel control method for a general class of nonlinear systems using fuzzy logic systems (FLSs) is presertted.Indirect and direct methods are combined to design the adaptive fuzzy output feedback controller and a high-gain observer is used to estimate the derivatives of the system output. The closed-loop system is proven to be semiglobally uniformly ultimately bounded. In addition, it is shown that if the approximation accuracy of the fuzzy logic system is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussion.

  12. Solving Fuzzy Nonlinear Volterra-Fredholm Integral Equations by Using Homotopy Analysis and Adomian Decomposition Methods

    Directory of Open Access Journals (Sweden)

    Shadan Sadigh Behzadi

    2011-12-01

    Full Text Available In this paper, Adomian decomposition method (ADM and homotopy analysis method (HAM are proposed to solving the fuzzy nonlinear Volterra-Fredholm integral equation of the second kind$(FVFIE-2$. we convert a fuzzy nonlinear Volterra-Fredholm integral equation to a nonlinear system of Volterra-Fredholm integral equation in crisp case. we use ADM , HAM and find the approximate solution of this system and hence obtain an approximation for fuzzy solution of the nonlinear fuzzy Volterra-Fredholm integral equation. Also, the existence and uniqueness of the solution and convergence of the proposed methods are proved. Examples is given and the results reveal that homotopy analysis method is very effective and simple compared with the Adomian decomposition method.

  13. A fast method for computing the centroid of a type-2 fuzzy set.

    Science.gov (United States)

    Wu, Hsin-Jung; Su, Yao-Lung; Lee, Shie-Jue

    2012-06-01

    Type reduction does the work of computing the centroid of a type-2 fuzzy set. The result is a type-1 fuzzy set from which a corresponding crisp number can then be obtained through defuzzification. Type reduction is one of the major operations involved in type-2 fuzzy inference. Therefore, making type reduction efficient is a significant task in the application of type-2 fuzzy systems. Liu introduced a horizontal slice representation, called the α-plane representation, and proposed a type-reduction method for a type-2 fuzzy set. By exploring some useful properties of the α-plane representation and of the type reduction for interval type-2 fuzzy sets, a fast method is developed for computing the centroid of a type-2 fuzzy set. The number of computations and comparisons involved is greatly reduced. Convergence in each iteration can then speed up, and type reduction can be done much more efficiently. The effectiveness of the proposed method is analyzed mathematically and demonstrated by experimental results.

  14. Single pass kernel -means clustering method

    Indian Academy of Sciences (India)

    T Hitendra Sarma; P Viswanath; B Eswara Reddy

    2013-06-01

    In unsupervised classification, kernel -means clustering method has been shown to perform better than conventional -means clustering method in identifying non-isotropic clusters in a data set. The space and time requirements of this method are $O(n^2)$, where is the data set size. Because of this quadratic time complexity, the kernel -means method is not applicable to work with large data sets. The paper proposes a simple and faster version of the kernel -means clustering method, called single pass kernel k-means clustering method. The proposed method works as follows. First, a random sample $\\mathcal{S}$ is selected from the data set $\\mathcal{D}$. A partition $\\Pi_{\\mathcal{S}}$ is obtained by applying the conventional kernel -means method on the random sample $\\mathcal{S}$. The novelty of the paper is, for each cluster in $\\Pi_{\\mathcal{S}}$, the exact cluster center in the input space is obtained using the gradient descent approach. Finally, each unsampled pattern is assigned to its closest exact cluster center to get a partition of the entire data set. The proposed method needs to scan the data set only once and it is much faster than the conventional kernel -means method. The time complexity of this method is $O(s^2+t+nk)$ where is the size of the random sample $\\mathcal{S}$, is the number of clusters required, and is the time taken by the gradient descent method (to find exact cluster centers). The space complexity of the method is $O(s^2)$. The proposed method can be easily implemented and is suitable for large data sets, like those in data mining applications. Experimental results show that, with a small loss of quality, the proposed method can significantly reduce the time taken than the conventional kernel -means clustering method. The proposed method is also compared with other recent similar methods.

  15. Research on Fuzzy Clustering Validity in Web Text Mining%Web文本挖掘中模糊聚类的有效性评价研究

    Institute of Scientific and Technical Information of China (English)

    罗琪

    2012-01-01

    本文研究了基于模糊聚类的Web文本挖掘和模糊聚类有效性评价函数,并将其应用于Web文本挖掘中模糊聚类有效性评价.仿真实验表明该方法有一定的准确性和可行性.%This paper studies web documents mining based on fuzzy clustering and validity evaluation function, and puts forward to applying validity evaluation function into evaluation of web text mining. The experiments show that FKCM can effectively improve the precision of web text clustering; the method is feasible in web documents mining. The result of emulation examinations indicates that the method has certain feasibility and accuracy.

  16. A Novel Multicriteria Group Decision Making Approach With Intuitionistic Fuzzy SIR Method

    CERN Document Server

    Chai, Junyi

    2011-01-01

    The superiority and inferiority ranking (SIR) method is a generation of the well-known PROMETHEE method, which can be more efficient to deal with multi-criterion decision making (MCDM) problem. Intuitionistic fuzzy sets (IFSs), as an important extension of fuzzy sets (IFs), include both membership functions and non-membership functions and can be used to, more precisely describe uncertain information. In real world, decision situations are usually under uncertain environment and involve multiple individuals who have their own points of view on handing of decision problems. In order to solve uncertainty group MCDM problem, we propose a novel intuitionistic fuzzy SIR method in this paper. This approach uses intuitionistic fuzzy aggregation operators and SIR ranking methods to handle uncertain information; integrate individual opinions into group opinions; make decisions on multiple-criterion; and finally structure a specific decision map. The proposed approach is illustrated in a simulation of group decision ma...

  17. Intuitionistic fuzzy entropy and distance measure based TOPSIS method for multi-criteria decision making

    Directory of Open Access Journals (Sweden)

    Deepa Joshi

    2014-07-01

    Full Text Available In this paper, an intuitionistic fuzzy TOPSIS method for multi-criteria decision making (MCDM problem to rank the alternatives is proposed. The proposed method is based on distance measure and intuitionistic fuzzy entropy. The proposed method also uses conversion theorem to convert fuzzy set to intuitionistic fuzzy set given by Jurio et al. (2010. A real case study is taken as an example to find the ranking of four organizations: Bajaj steel, H.D.F.C. bank, Tata steel and Infotech enterprises using real data. In order to compare the different rankingS, they are applied in a portfolio selection problem. Different portfolios are constructed and are analyzed for their risk and return. It is observed that if the portfolios are constructed using the ranking obtained with proposed method, the return is increased with slight increment in risk.

  18. Design Method for the Magnetic Bearing Control System with Fuzzy-PID Approach

    Institute of Scientific and Technical Information of China (English)

    XU Chun-guang; L(U) Dong-ming; HAO Juan

    2008-01-01

    The five degree freedom magnetic bearing is researched and its structure and working principles are introduced also.Based on the fuzzy control technology,combining fuzzy algorithm and PID control method,identifying the transition process mode of the online system to get the PID parameters'self-adjusting,the magnetic bearing system's Fuzzy-PID nonlinear controller is designed by analyzing the system control demands.The Fuzzy-PID nonlinear controller can deal with the magnetic bearing system's open loop instability and strong nonlinearity,and the approach could improve the system's rapidity,adaptability,stability and dynamic characteristics.Comparative analysis and experiments are conducted between linear PID and nonlinear fuzzyPID control methods,the results show that the fuzzy-PID controller is better,and the five-freedom magnetic bearing's rotary precision experiments are conducted by the fuzzy-PID controller,it satisfies the control rotary precision demands and realizes the bearing's steady floating and rotating.

  19. Research on Coordinated Robotic Motion Control Based on Fuzzy Decoupling Method in Fluidic Environments

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2014-01-01

    Full Text Available The underwater recovery of autonomous underwater vehicles (AUV is a process of 6-DOF motion control, which is related to characteristics with strong nonlinearity and coupling. In the recovery mission, the vehicle requires high level control accuracy. Considering an AUV called BSAV, this paper established a kinetic model to describe the motion of AUV in the horizontal plane, which consisted of nonlinear equations. On the basis of this model, the main coupling variables were analyzed during recovery. Aiming at the strong coupling problem between the heading control and sway motion, we designed a decoupling compensator based on the fuzzy theory and the decoupling theory. We analyzed to the rules of fuzzy compensation, the input and output membership functions of fuzzy compensator, through compose operation and clear operation of fuzzy reasoning, and obtained decoupling compensation quantity. Simulation results show that the fuzzy decoupling controller effectively reduces the overshoot of the system, and improves the control precision. Through the water tank experiments and analysis of experimental data, the effectiveness and feasibility of AUV recovery movement coordinated control based on fuzzy decoupling method are validated successful, and show that the fuzzy decoupling control method has a high practical value in the recovery mission.

  20. Intuitionistic Fuzzy Cycles and Intuitionistic Fuzzy Trees

    Science.gov (United States)

    Alshehri, N. O.

    2014-01-01

    Connectivity has an important role in neural networks, computer network, and clustering. In the design of a network, it is important to analyze connections by the levels. The structural properties of intuitionistic fuzzy graphs provide a tool that allows for the solution of operations research problems. In this paper, we introduce various types of intuitionistic fuzzy bridges, intuitionistic fuzzy cut vertices, intuitionistic fuzzy cycles, and intuitionistic fuzzy trees in intuitionistic fuzzy graphs and investigate some of their interesting properties. Most of these various types are defined in terms of levels. We also describe comparison of these types. PMID:24701155

  1. An efficient computer based wavelets approximation method to solve Fuzzy boundary value differential equations

    Science.gov (United States)

    Alam Khan, Najeeb; Razzaq, Oyoon Abdul

    2016-03-01

    In the present work a wavelets approximation method is employed to solve fuzzy boundary value differential equations (FBVDEs). Essentially, a truncated Legendre wavelets series together with the Legendre wavelets operational matrix of derivative are utilized to convert FB- VDE into a simple computational problem by reducing it into a system of fuzzy algebraic linear equations. The capability of scheme is investigated on second order FB- VDE considered under generalized H-differentiability. Solutions are represented graphically showing competency and accuracy of this method.

  2. APPLICATION OF FUZZY CONTROL METHOD WITH SELF-TUNING FACTOR IN JIGGERS DISCHARGING

    Institute of Scientific and Technical Information of China (English)

    杨洁明; 魏晋宏; 刘素芬

    2000-01-01

    Adopting the strategy of fuzzy control with self-tuning factor within whole universe of discourse, a kind of fuzzy control method for jigger discharging is put forward. This method has many advantages over the conventional PID controller in terms of response speed, stability and robustness. It is effective to restrain the jig bed from over-thick or empty, and the stability of the bed is markedly improved. The good results are obtained in factory tests.

  3. A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering.

    Science.gov (United States)

    Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine

    2015-12-01

    Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an "enhanced multivariate degradation modeling," which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.

  4. Using Quadtree Algorithm for Improving Fuzzy C-means Method in Image Segmentation

    OpenAIRE

    Zahra Ghorbanzad; Farshid Babapour

    2012-01-01

    Image segmentation is an essential processing step for much image application and there are a large number of segmentation techniques. A new algorithm for image segmentation called Quad tree fuzzy c-means (QFCM) is presented I this work. The key idea in our approach is a Quad tree function combined with fuzzy c-means algorithm. In this article we also discuss the advantages and disadvantages of other image segmenting methods like: k-means, c-means, and blocked fuzzy c-means. Different experim...

  5. Robust Fuzzy PD Method with Parallel Computed Fuel Ratio Estimation Applied to Automotive Engine

    Directory of Open Access Journals (Sweden)

    Farzin Piltan

    2013-07-01

    Full Text Available Both fuzzy logic and computed fuel ratio can compensate the steady-state error of proportional-derivative (PD method. This paper presents parallel computed fuel ratio compensation for fuzzy plus PID control management with application to internal combustion (IC engine. The asymptotic stability of fuzzy plus PID control methodology with first-order computed fuel ratio estimation in the parallel structure is proven. For the parallel structure, the finite time convergence with a super-twisting second-order sliding-mode is guaranteed.

  6. Transportation Problem with Pentagonal Intuitionistic Fuzzy Numbers Solved Using Ranking Technique and Russell’s Method

    Directory of Open Access Journals (Sweden)

    Dr.M.S.Annie Christi,

    2016-02-01

    Full Text Available This paper presents a solution methodology for transportation problem in an intuitionistic fuzzy environment in which cost are represented by pentagonal intuitionistic fuzzy numbers. Transportation problem is a particular class of linear programming, which is associated with day to day activities in our real life. It helps in solving problems on distribution and transportation of resources from one place to another. The objective is to satisfy the demand at destination from the supply constraints at the minimum transportation cost possible. The problem is solved using a ranking technique called Accuracy function for pentagonal intuitionistic fuzzy numbers and Russell’s Method. An illustrative example is given to verify this approach.

  7. Predictive fuzzy reasoning method for time series stock market data mining

    Science.gov (United States)

    Khokhar, Rashid H.; Md Sap, Mohd Noor

    2005-03-01

    Data mining is able to uncover hidden patterns and predict future trends and behaviors in financial markets. In this research we approach quantitative time series stock selection as a data mining problem. We present another modification of extraction of weighted fuzzy production rules (WFPRs) from fuzzy decision tree by using proposed similarity-based fuzzy reasoning method called predictive reasoning (PR) method. In proposed predictive reasoning method weight parameter can be assigned to each proposition in the antecedent of a fuzzy production rule (FPR) and certainty factor (CF) to each rule. Certainty factors are calculated by using some important variables like effect of other companies, effect of other local stock market, effect of overall world situation, and effect of political situation from stock market. The predictive FDT has been tested using three data sets including KLSE, NYSE and LSE. The experimental results show that WFPRs rules have high learning accuracy and also better predictive accuracy of stock market time series data.

  8. Finding optimal step of fuzzy Newton-Cotes integration rules by using the CESTAC method

    Directory of Open Access Journals (Sweden)

    Samad Noeiaghdam

    2017-08-01

    Full Text Available The aim of this work, is to evaluate the value of a fuzzy integral by applying the Newton-Cotes integration rules via a reliable scheme. In order to perform the numerical examples, the CADNA (Control of Accuracy and Debugging for Numerical Applications library and the CESTAC (Controle et Estimation Stochastique des Arrondis de Calculs method are applied based on the stochastic arithmetic. By using this method, the optimal number of points in the fuzzy numerical integration rules and the optimal approximate solution are obtained. Also, the accuracy of the fuzzy quadrature rules are discussed. An algorithm is given to illustrate the implementation of the method. In this case, the termination criterion is considered as the Hausdorff distance between two sequential results to be an informatical zero. Two sample fuzzy integrals are evaluated based on the proposed algorithm to show the importance and advantage of using the stochastic arithmetic in place of the floating-point arithmetic.

  9. Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches.

    Science.gov (United States)

    Bolin, Jocelyn H; Edwards, Julianne M; Finch, W Holmes; Cassady, Jerrell C

    2014-01-01

    Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

  10. Applications of Cluster Analysis to the Creation of Perfectionism Profiles: A Comparison of two Clustering Approaches

    Directory of Open Access Journals (Sweden)

    Jocelyn H Bolin

    2014-04-01

    Full Text Available Although traditional clustering methods (e.g., K-means have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

  11. A new co-operative inversion strategy via fuzzy clustering technique applied to seismic and magnetotelluric data

    Science.gov (United States)

    Thong Kieu, Duy; Kepic, Anton

    2015-04-01

    Geophysical inversion produces very useful images of earth parameters; however, inversion results usually suffer from inherent non-uniqueness: many subsurface models with different structures and parameters can explain the measurements. To reduce the ambiguity, extra information about the earth's structure and physical properties is needed. This prior information can be extracted from geological principles, prior petrophysical information from well logs, and complementary information from other geophysical methods. Any technique used to constrain inversion should be able to integrate the prior information and to guide updating inversion process in terms of the geological model. In this research, we have adopted fuzzy c-means (FCM) clustering technique for this purpose. FCM is a clustering method that allows us to divide the model of physical parameters into a few clusters of representative values that also may relate to geological units based on the similarity of the geophysical properties. This exploits the fact that in many geological environments the earth is comprised of a few distinctive rock units with different physical properties. Therefore FCM can provide a platform to constrain geophysical inversion, and should tend to produce models that are geologically meaningful. FCM was incorporated in both separate and co-operative inversion processing of seismic and magnetotelluric (MT) data with petrophysical constraints. Using petrophysical information through FCM assists the inversion to build a reliable earth model. In this algorithm, FCM plays a role of guider; it uses the prior information to drive the model update process, and also forming an earth model filled with rocks units rather than smooth transitions when the boundary is in doubt. Where petrophysical information from well logs or core measurement is not locally available the cluster petrophysics may be solved for in inversion as well if some knowledge of how many distinctive geological exist. A

  12. HYBRID OF FUZZY CLUSTERING NEURAL NETWORK OVER NSL DATASET FOR INTRUSION DETECTION SYSTEM

    Directory of Open Access Journals (Sweden)

    Dahlia Asyiqin Ahmad Zainaddin

    2013-01-01

    Full Text Available Intrusion Detection System (IDS is one of the component that take part in the system defence, to identify abnormal activities happening in the computer system. Nowadays, IDS facing composite demands to defeat modern attack activities from damaging the computer systems. Anomaly-Based IDS examines ongoing traffic, activity, transactions and behavior in order to identify intrusions by detecting anomalies. These technique identifies activities which degenerates from the normal behaviours. In recent years, data mining approach for intrusion detection have been advised and used. The approach such as Genetic Algorithms , Support Vector Machines, Neural Networks as well as clustering has resulted in high accuracy and good detection rates but with moderate false alarm on novel attacks. Many researchers also have proposed hybrid data mining techniques. The previous resechers has intoduced the combination of Fuzzy Clustering and Artificial Neural Network. However, it was tested only on randomn selection of KDDCup 1999 dataset. In this study the framework experiment introduced, has been used over the NSL dataset to test the stability and reliability of the technique. The result of precision, recall and f-value rate is compared with previous experiment. Both dataset covers four types of main attacks, which are Derial of Services (DoS, User to Root (U2R, Remote to Local (R2L and Probe. Results had guarenteed that the hybrid approach performed better detection especially for low frequent over NSL datataset compared to original KDD dataset, due to the removal of redundancy and uncomplete elements in the original dataset. This electronic document is a “live” template. The various components of your paper [title, text, tables, figures and references] are already defined on the style sheet, as illustrated by the portions given in this document.

  13. A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection

    Directory of Open Access Journals (Sweden)

    Burhan Ergen

    2014-01-01

    Full Text Available This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT and Magnetic Resonance Imaging (MRI devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.

  14. A fusion method of Gabor wavelet transform and unsupervised clustering algorithms for tissue edge detection.

    Science.gov (United States)

    Ergen, Burhan

    2014-01-01

    This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.

  15. Using a fuzzy comprehensive evaluation method to determine product usability: A proposed theoretical framework.

    Science.gov (United States)

    Zhou, Ronggang; Chan, Alan H S

    2017-01-01

    In order to compare existing usability data to ideal goals or to that for other products, usability practitioners have tried to develop a framework for deriving an integrated metric. However, most current usability methods with this aim rely heavily on human judgment about the various attributes of a product, but often fail to take into account of the inherent uncertainties in these judgments in the evaluation process. This paper presents a universal method of usability evaluation by combining the analytic hierarchical process (AHP) and the fuzzy evaluation method. By integrating multiple sources of uncertain information during product usability evaluation, the method proposed here aims to derive an index that is structured hierarchically in terms of the three usability components of effectiveness, efficiency, and user satisfaction of a product. With consideration of the theoretical basis of fuzzy evaluation, a two-layer comprehensive evaluation index was first constructed. After the membership functions were determined by an expert panel, the evaluation appraisals were computed by using the fuzzy comprehensive evaluation technique model to characterize fuzzy human judgments. Then with the use of AHP, the weights of usability components were elicited from these experts. Compared to traditional usability evaluation methods, the major strength of the fuzzy method is that it captures the fuzziness and uncertainties in human judgments and provides an integrated framework that combines the vague judgments from multiple stages of a product evaluation process.

  16. Clustering-based Spam Image Filtering Considering Fuzziness of the Spam Image

    Directory of Open Access Journals (Sweden)

    Master Prince

    2016-12-01

    Full Text Available If there are pros, corns are always there. As email becomes a part of individual’s need in our busy life with its benefits, it has negative aspect too by means of email spamming. Nowadays images with embedded text called image spamming have been used by the spammers as effective text spam filtering methods already been introduced. Tracking and stopping spam become challenge in the internet world because of versatility in the spam images. In this paper a novel model AFSIF (Autonomous Fuzzy Spam Image Filter has been introduced. The basic idea behind AFSIF is, an spam image can combine several basic features of different spam images, so feature fusion weight of the image has been generated, which keeps combined feature of spam images and user preference as well. Here user preference has not been applied separately; it is used to calculate the fusion weight in terms of predefined topics (rule table.

  17. A sequential fuzzy diagnosis method for rotating machinery using ant colony optimization and possibility theory

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Hao; Ping, Xueliang; Cao, Yi; Lie, Ke [Jiangnan University, Wuxi (China); Chen, Peng [Mie University, Mie (Japan); Wang, Huaqing [Beijing University, Beijing (China)

    2014-04-15

    This study proposes a novel intelligent fault diagnosis method for rotating machinery using ant colony optimization (ACO) and possibility theory. The non-dimensional symptom parameters (NSPs) in the frequency domain are defined to reflect the features of the vibration signals measured in each state. A sensitive evaluation method for selecting good symptom parameters using principal component analysis (PCA) is proposed for detecting and distinguishing faults in rotating machinery. By using ACO clustering algorithm, the synthesizing symptom parameters (SSP) for condition diagnosis are obtained. A fuzzy diagnosis method using sequential inference and possibility theory is also proposed, by which the conditions of the machinery can be identified sequentially. Lastly, the proposed method is compared with a conventional neural networks (NN) method. Practical examples of diagnosis for a V-belt driving equipment used in a centrifugal fan are provided to verify the effectiveness of the proposed method. The results verify that the faults that often occur in V-belt driving equipment, such as a pulley defect state, a belt defect state and a belt looseness state, are effectively identified by the proposed method, while these faults are difficult to detect using conventional NN.

  18. A Clustering and SVM Regression Learning-Based Spatiotemporal Fuzzy Logic Controller with Interpretable Structure for Spatially Distributed Systems

    Directory of Open Access Journals (Sweden)

    Xian-xia Zhang

    2012-01-01

    Full Text Available Many industrial processes and physical systems are spatially distributed systems. Recently, a novel 3-D FLC was developed for such systems. The previous study on the 3-D FLC was concentrated on an expert knowledge-based approach. However, in most of situations, we may lack the expert knowledge, while input-output data sets hidden with effective control laws are usually available. Under such circumstance, a data-driven approach could be a very effective way to design the 3-D FLC. In this study, we aim at developing a new 3-D FLC design methodology based on clustering and support vector machine (SVM regression. The design consists of three parts: initial rule generation, rule-base simplification, and parameter learning. Firstly, the initial rules are extracted by a nearest neighborhood clustering algorithm with Frobenius norm as a distance. Secondly, the initial rule-base is simplified by merging similar 3-D fuzzy sets and similar 3-D fuzzy rules based on similarity measure technique. Thirdly, the consequent parameters are learned by a linear SVM regression algorithm. Additionally, the universal approximation capability of the proposed 3-D fuzzy system is discussed. Finally, the control of a catalytic packed-bed reactor is taken as an application to demonstrate the effectiveness of the proposed 3-D FLC design.

  19. Decentralized robust stabilization of discrete-time fuzzy large-scale systems with parametric uncertainties: a LMI method

    Institute of Scientific and Technical Information of China (English)

    Zhang Yougang; Xu Bugong

    2006-01-01

    Decentralized robust stabilization problem of discrete-time fuzzy large-scale systems with parametric uncertainties is considered. This uncertain fuzzy large-scale system consists of N interconnected T-S fuzzy subsystems, and the parametric uncertainties are unknown but norm-bounded. Based on Lyapunov stability theory and decentralized control theory of large-scale system, the design schema of decentralized parallel distributed compensation (DPDC) fuzzy controllers to ensure the asymptotic stability of the whole fuzzy large-scale system is proposed. The existence conditions for these controllers take the forms of LMIs. Finally a numerical simulation example is given to show the utility of the method proposed.

  20. Linear programming models and methods of matrix games with payoffs of triangular fuzzy numbers

    CERN Document Server

    Li, Deng-Feng

    2016-01-01

    This book addresses two-person zero-sum finite games in which the payoffs in any situation are expressed with fuzzy numbers. The purpose of this book is to develop a suite of effective and efficient linear programming models and methods for solving matrix games with payoffs in fuzzy numbers. Divided into six chapters, it discusses the concepts of solutions of matrix games with payoffs of intervals, along with their linear programming models and methods. Furthermore, it is directly relevant to the research field of matrix games under uncertain economic management. The book offers a valuable resource for readers involved in theoretical research and practical applications from a range of different fields including game theory, operational research, management science, fuzzy mathematical programming, fuzzy mathematics, industrial engineering, business and social economics. .

  1. Application of neuro-fuzzy methods to gamma spectroscopy

    Science.gov (United States)

    Grelle, Austin L.

    Nuclear non-proliferation activities are an essential part of national security activities both domestic and abroad. The safety of the public in densely populated environments such as urban areas or large events can be compromised if devices using special nuclear materials are present. Therefore, the prompt and accurate detection of these materials is an important topic of research, in which the identification of normal conditions is also of importance. With gamma-ray spectroscopy, these conditions are identified as the radiation background, which though being affected by a multitude of factors is ever present. Therefore, in nuclear non-proliferation activities the accurate identification of background is important. With this in mind, a method has been developed to utilize aggregate background data to predict the background of a location through the use of an Artificial Neural Network (ANN). After being trained on background data, the ANN is presented with nearby relevant gamma-ray spectroscopy data---as identified by a Fuzzy Inference System - to create a predicted background spectra to compare to a measured spectra. If a significant deviation exists between the predicted and measured data, the method alerts the user such that a more thorough investigation can take place. Research herein focused on data from an urban setting in which the number of false positives was observed to be 28 out of a total of 987, representing 2.94% error. The method therefore currently shows a high rate of false positives given the current configuration, however there are promising steps that can be taken to further minimize this error. With this in mind, the method stands as a potentially significant tool in urban nuclear nonproliferation activities.

  2. Moment Method Based on Fuzzy Reliability Sensitivity Analysis for a Degradable Structural System

    Institute of Scientific and Technical Information of China (English)

    Song Jun; Lu Zhenzhou

    2008-01-01

    For a degradable structural system with fuzzy failure region, a moment method based on fuzzy reliability sensitivity algorithm is presented. According to the value assignment of porformance function, the integral region for calculating the fuzzy failure probability is first split into a series of subregions in which the membership function values of the performance function within the fuzzy failure region can be approximated by a set of constants. The fuzzy failure probability is then transformed into a sum of products oftbe random failure probabilities and the approximate constants of the membership function in the subregions. Furthermore, the fuzzy reliability sensitivity analysis is transformed into a series of random reliability sensitivity analysis, and the random reliability sensitivity can be obtained by the constructed moment method. The primary advantages of the presented method include higher efficiency for implicit performance function with low and medium dimensionality and wide applicability to multiple failure modes and nonnormal basic random variables. The limitation is that the required computation effort grows exponentially with the increase of dimensionality of the basic random vari-able; hence, it is not suitable for high dimensionality problem. Compared with the available methods, the presented one is pretty com-petitive in the case that the dimensionality is lower than 10. The presented examples are used to verify the advantages and indicate the limitations.

  3. Choosing the best method of depreciating assets and after-tax economic analysis under uncertainty using fuzzy approach

    Directory of Open Access Journals (Sweden)

    Saeed Khalili

    2014-08-01

    Full Text Available In the past, different methods for asset depreciation have been defined but most of these procedures deal with certain parameters and inputs. The availability of certain parameters in many real world situations is difficult and sometimes impossible. The primary objective of this paper is to obtain methods for calculating depreciation where some of the defined parameters are under uncertainty. Hence, by using the fuzzy science basics, extension principle and α-cut technique, we rewrite some classic methods for calculating depreciation in fuzzy form. Then, for comparing the methods of fuzzy depreciation under uncertain conditions by using the formula of calculating the Fuzzy Present worth (FPW, the Present worth of Tax saving (PWTS of any aforementioned methods has been obtained. Finally, since the amount of tax savings achieved for each of the methods is a fuzzy number, one of the fuzzy prioritization methods is used in order to select the best depreciation technique.

  4. A novel prediction method for back pressure based on fuzzy inference theory

    Science.gov (United States)

    Chen, Guanghua; Zhang, Kunting; Qi, Hongyuan; Nan, Bingshen

    2017-01-01

    In order to solve the problem of back pressure set unreasonable in direct air-cooling unit, a back-pressure-fuzzy-inference machine is established in this paper, of which the environmental temperature and wind speed are the inputs, and the optimal back pressure is the output. The feasibility of the novel method is verified by simulation and experimental results, and the accuracy of back pressure fuzzy prediction can satisfy the operating requirements.

  5. Solution of second order linear fuzzy difference equation by Lagrange's multiplier method

    Directory of Open Access Journals (Sweden)

    Sankar Prasad Mondal

    2016-06-01

    Full Text Available In this paper we execute the solution procedure for second order linear fuzzy difference equation by Lagrange's multiplier method. In crisp sense the difference equation are easy to solve, but when we take in fuzzy sense it forms a system of difference equation which is not so easy to solve. By the help of Lagrange's multiplier we can solved it easily. The results are illustrated by two different numerical examples and followed by two applications.

  6. Fuzzy Cluster Analysis on Railway Logistics Node Planning%铁路物流节点规划模糊聚类分析

    Institute of Scientific and Technical Information of China (English)

    孙海涛; 李仲秋

    2014-01-01

    Modern logistics as an advanced organization and management technique,has become an important driving force to economic development. For the current lack of railway logistics node status of integrated development planning research,use fuzzy cluster analysis method for researching. Firstly,establish factor indicator system,and manifest through the analysis of hierarchical structure model. Then, establish mathematical model according to fuzzy cluster analysis step,including an index evaluation,dimensionless processing,fuzzy es-tablishing relation,fuzzy cluster and so on. Finally,through the examples planning illustrate the index system and mathematical model for specific application. The content of this study is important for the scientific planning and hierarchical classification of railway logistics node,the rational division of labor and coordination layout between different nodes of transportation logistics modes.%现代物流作为先进的组织方式和管理技术,已经成为经济发展重要的推动力量。针对目前缺乏对铁路物流节点综合发展规划研究的现状,文中运用模糊聚类分析方法进行研究。首先,建立影响因素指标体系,并通过递阶层次分析结构模型表现出来;然后,按模糊聚类分析的步骤建立数学模型,包括一级指标评价、无量纲化处理、建立模糊关系、模糊聚类等;最后,通过规划实例来说明指标体系和数学模型的具体应用。文中的研究内容,对于科学规划分层分类的铁路物流节点,实现不同运输方式物流节点之间的合理分工与协调布局具有重要意义。

  7. Saddlepoint approximation based line sampling method for uncertainty propagation in fuzzy and random reliability analysis

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    For structural system with random basic variables as well as fuzzy basic variables,uncertain propagation from two kinds of basic variables to the response of the structure is investigated.A novel algorithm for obtaining membership function of fuzzy reliability is presented with saddlepoint approximation(SA)based line sampling method.In the presented method,the value domain of the fuzzy basic variables under the given membership level is firstly obtained according to their membership functions.In the value domain of the fuzzy basic variables corresponding to the given membership level,bounds of reliability of the structure response satisfying safety requirement are obtained by employing the SA based line sampling method in the reduced space of the random variables.In this way the uncertainty of the basic variables is propagated to the safety measurement of the structure,and the fuzzy membership function of the reliability is obtained.Compared to the direct Monte Carlo method for propagating the uncertainties of the fuzzy and random basic variables,the presented method can considerably improve computational efficiency with acceptable precision.The presented method has wider applicability compared to the transformation method,because it doesn’t limit the distribution of the variable and the explicit expression of performance function, and no approximation is made for the performance function during the computing process.Additionally,the presented method can easily treat the performance function with cross items of the fuzzy variable and the random variable,which isn’t suitably approximated by the existing transformation methods.Several examples are provided to illustrate the advantages of the presented method.

  8. Introduction to Fuzzy Set Theory

    Science.gov (United States)

    Kosko, Bart

    1990-01-01

    An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

  9. Prediction system of hydroponic plant growth and development using algorithm Fuzzy Mamdani method

    Science.gov (United States)

    Sudana, I. Made; Purnawirawan, Okta; Arief, Ulfa Mediaty

    2017-03-01

    Hydroponics is a method of farming without soil. One of the Hydroponic plants is Watercress (Nasturtium Officinale). The development and growth process of hydroponic Watercress was influenced by levels of nutrients, acidity and temperature. The independent variables can be used as input variable system to predict the value level of plants growth and development. The prediction system is using Fuzzy Algorithm Mamdani method. This system was built to implement the function of Fuzzy Inference System (Fuzzy Inference System/FIS) as a part of the Fuzzy Logic Toolbox (FLT) by using MATLAB R2007b. FIS is a computing system that works on the principle of fuzzy reasoning which is similar to humans' reasoning. Basically FIS consists of four units which are fuzzification unit, fuzzy logic reasoning unit, base knowledge unit and defuzzification unit. In addition to know the effect of independent variables on the plants growth and development that can be visualized with the function diagram of FIS output surface that is shaped three-dimensional, and statistical tests based on the data from the prediction system using multiple linear regression method, which includes multiple linear regression analysis, T test, F test, the coefficient of determination and donations predictor that are calculated using SPSS (Statistical Product and Service Solutions) software applications.

  10. Hybrid Multicriteria Group Decision Making Method for Information System Project Selection Based on Intuitionistic Fuzzy Theory

    Directory of Open Access Journals (Sweden)

    Jian Guo

    2013-01-01

    Full Text Available Information system (IS project selection is of critical importance to every organization in dynamic competing environment. The aim of this paper is to develop a hybrid multicriteria group decision making approach based on intuitionistic fuzzy theory for IS project selection. The decision makers’ assessment information can be expressed in the form of real numbers, interval-valued numbers, linguistic variables, and intuitionistic fuzzy numbers (IFNs. All these evaluation pieces of information can be transformed to the form of IFNs. Intuitionistic fuzzy weighted averaging (IFWA operator is utilized to aggregate individual opinions of decision makers into a group opinion. Intuitionistic fuzzy entropy is used to obtain the entropy weights of the criteria. TOPSIS method combined with intuitionistic fuzzy set is proposed to select appropriate IS project in group decision making environment. Finally, a numerical example for information system projects selection is given to illustrate application of hybrid multi-criteria group decision making (MCGDM method based on intuitionistic fuzzy theory and TOPSIS method.

  11. Rough similarity degree and rough close degree in rough fuzzy sets and the applications

    Institute of Scientific and Technical Information of China (English)

    Li Jian; Xu Xiaojing; Shi Kaiquan

    2008-01-01

    Based on rough similarity degree of rough sets and close degree of fuzzy sets,the definitions of rough similarity degree and rough close degree of rough fuzzy sets are given,which can be used to measure the similar degree between two rough fuzzy sets.The properties and theorems are listed.Using the two new measures,the method of clustering in the rough fuzzy system can be obtained.After clustering,the new fuzzy sample can be recognized by the principle of maximal similarity degree.

  12. Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method.

    Science.gov (United States)

    Sim, Jaehyun; Kim, Seung-Yeon; Lee, Julian

    2005-06-15

    The solvent accessibility of amino acid residues plays an important role in tertiary structure prediction, especially in the absence of significant sequence similarity of a query protein to those with known structures. The prediction of solvent accessibility is less accurate than secondary structure prediction in spite of improvements in recent researches. The k-nearest neighbor method, a simple but powerful classification algorithm, has never been applied to the prediction of solvent accessibility, although it has been used frequently for the classification of biological and medical data. We applied the fuzzy k-nearest neighbor method to the solvent accessibility prediction, using PSI-BLAST profiles as feature vectors, and achieved high prediction accuracies. With leave-one-out cross-validation on the ASTRAL SCOP reference dataset constructed by sequence clustering, our method achieved 64.1% accuracy for a 3-state (buried/intermediate/exposed) prediction (thresholds of 9% for buried/intermediate and 36% for intermediate/exposed) and 86.7, 82.0, 79.0 and 78.5% accuracies for 2-state (buried/exposed) predictions (thresholds of each 0, 5, 16 and 25% for buried/exposed), respectively. Our method also showed slightly better accuracies than other methods by about 2-5% on the RS126 dataset and a benchmarking dataset with 229 proteins. Program and datasets are available at http://biocom1.ssu.ac.kr/FKNNacc/ jul@ssu.ac.kr.

  13. Genetic algorithm with fuzzy clustering for optimization of nuclear reactor problems; Um algoritmo genetico com clusterizacao nebulosa para a otimizacao de problemas de reatores nucleares

    Energy Technology Data Exchange (ETDEWEB)

    Machado, Marcelo Dornellas; Sacco, Wagner Figueiredo; Schirru, Roberto [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia. Programa de Engenharia Nuclear

    2000-07-01

    Genetic Algorithms (GAs) are biologically motivated adaptive systems which have been used, with good results, in function optimization. However, traditional GAs rapidly push an artificial population toward convergence. That is, all individuals in the population soon become nearly identical. Niching Methods allow genetic algorithms to maintain a population of diverse individuals. GAs that incorporate these methods are capable of locating multiple, optimal solutions within a single population. The purpose of this study is to introduce a new niching technique based on the fuzzy clustering method FCM, bearing in mind its eventual application in nuclear reactor related problems, specially the nuclear reactor core reload one, which has multiple solutions. tests are performed using widely known test functions and their results show that the new method is quite promising, specially to a future application in real world problems like the nuclear reactor core reload. (author)

  14. Fuzzy jets

    Energy Technology Data Exchange (ETDEWEB)

    Mackey, Lester [Department of Statistics, Stanford University,Stanford, CA 94305 (United States); Nachman, Benjamin [Department of Physics, Stanford University,Stanford, CA 94305 (United States); SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States); Schwartzman, Ariel [SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States); Stansbury, Conrad [Department of Physics, Stanford University,Stanford, CA 94305 (United States)

    2016-06-01

    Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets, are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variables in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.

  15. Modelling of the automatic stabilization system of the aircraft course by a fuzzy logic method

    Science.gov (United States)

    Mamonova, T.; Syryamkin, V.; Vasilyeva, T.

    2016-04-01

    The problem of the present paper concerns the development of a fuzzy model of the system of an aircraft course stabilization. In this work modelling of the aircraft course stabilization system with the application of fuzzy logic is specified. Thus the authors have used the data taken for an ordinary passenger plane. As a result of the study the stabilization system models were realised in the environment of Matlab package Simulink on the basis of the PID-regulator and fuzzy logic. The authors of the paper have shown that the use of the method of artificial intelligence allows reducing the time of regulation to 1, which is 50 times faster than the time when standard receptions of the management theory are used. This fact demonstrates a positive influence of the use of fuzzy regulation.

  16. Simulation of thermal behavior of residential buildings using fuzzy active learning method

    Directory of Open Access Journals (Sweden)

    Masoud Taheri Shahraein

    2015-01-01

    Full Text Available In this paper, a fuzzy modeling technique called Modified Active Learning Method (MALM was introduced and utilized for fuzzy simulation of indoor and inner surface temperatures in residential buildings using meteorological data and its capability for fuzzy simulation was compared with other studies. The case studies for simulations were two residential apartments in the Fakouri and Rezashahr neighborhoods of Mashhad, Iran. The hourly inner surface and indoor temperature data were accumulated during measurements taken in 2010 and 2011 in different rooms of the apartments under heating and natural ventilation conditions. Hourly meteorological data (dry bulb temperature, wind speed and direction and solar radiation were measured by a meteorological station and utilized with zero to three hours lags as input variables for the simulation of inner surface and indoor temperatures. The results of simulations demonstrated the capability of MALM to be used for nonlinear fuzzy simulation of inner surface and indoor temperatures in residential apartments.

  17. Data Reduction Method for Categorical Data Clustering

    OpenAIRE

    Sánchez Garreta, José Salvador; Rendón, Eréndira; García, Rene A.; Abundez, Itzel; Gutiérrez, Citlalih; Gasca, Eduardo

    2008-01-01

    Categorical data clustering constitutes an important part of data mining; its relevance has recently drawn attention from several researchers. As a step in data mining, however, clustering encounters the problem of large amount of data to be processed. This article offers a solution for categorical clustering algorithms when working with high volumes of data by means of a method that summarizes the database. This is done using a structure called CM-tree. In order to test our metho...

  18. A Simplified Version of the Fuzzy Decision Method and its Comparison with the Paraconsistent Decision Method

    Science.gov (United States)

    de Carvalho, Fábio Romeu; Abe, Jair Minoro

    2010-11-01

    Two recent non-classical logics have been used to make decision: fuzzy logic and paraconsistent annotated evidential logic Et. In this paper we present a simplified version of the fuzzy decision method and its comparison with the paraconsistent one. Paraconsistent annotated evidential logic Et, introduced by Da Costa, Vago and Subrahmanian (1991), is capable of handling uncertain and contradictory data without becoming trivial. It has been used in many applications such as information technology, robotics, artificial intelligence, production engineering, decision making etc. Intuitively, one Et logic formula is type p(a, b), in which a and b belong to [0, 1] (real interval) and represent respectively the degree of favorable evidence (or degree of belief) and the degree of contrary evidence (or degree of disbelief) found in p. The set of all pairs (a; b), called annotations, when plotted, form the Cartesian Unitary Square (CUS). This set, containing a similar order relation of real number, comprises a network, called lattice of the annotations. Fuzzy logic was introduced by Zadeh (1965). It tries to systematize the knowledge study, searching mainly to study the fuzzy knowledge (you don't know what it means) and distinguish it from the imprecise one (you know what it means, but you don't know its exact value). This logic is similar to paraconsistent annotated one, since it attributes a numeric value (only one, not two values) to each proposition (then we can say that it is an one-valued logic). This number translates the intensity (the degree) with which the preposition is true. Let's X a set and A, a subset of X, identified by the function f(x). For each element x∈X, you have y = f(x)∈[0, 1]. The number y is called degree of pertinence of x in A. Decision making theories based on these logics have shown to be powerful in many aspects regarding more traditional methods, like the one based on Statistics. In this paper we present a first study for a simplified

  19. Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering.

    Science.gov (United States)

    Elazab, Ahmed; Wang, Changmiao; Jia, Fucang; Wu, Jianhuang; Li, Guanglin; Hu, Qingmao

    2015-01-01

    An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.

  20. Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering

    Directory of Open Access Journals (Sweden)

    Ahmed Elazab

    2015-01-01

    Full Text Available An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.

  1. Discussion on Fuzzy Bayes' Method for Parameter Estimation%参数估值的模糊Bayes方法探讨

    Institute of Scientific and Technical Information of China (English)

    马家蓉; 徐军

    2011-01-01

    在样本参数随机模糊估值的基础上,提出了基于概率论与模糊数学相结合的模糊Bayes方法推断样本参数的统计特征,并证明了模糊Bayes方法是常规Bayes方法的一个特例.%Based on the random-fuzzy method for treating sample parameter,Fuzzy Bayes' method combining probability theory and fuzzy mathematics was proposed to infer the statistical property of sample parameter.The Fuzzy Bayes' method was proved to be a special case of conventional Bayes' method.

  2. Quantum Monte Carlo methods and lithium cluster properties. [Atomic clusters

    Energy Technology Data Exchange (ETDEWEB)

    Owen, R.K.

    1990-12-01

    Properties of small lithium clusters with sizes ranging from n = 1 to 5 atoms were investigated using quantum Monte Carlo (QMC) methods. Cluster geometries were found from complete active space self consistent field (CASSCF) calculations. A detailed development of the QMC method leading to the variational QMC (V-QMC) and diffusion QMC (D-QMC) methods is shown. The many-body aspect of electron correlation is introduced into the QMC importance sampling electron-electron correlation functions by using density dependent parameters, and are shown to increase the amount of correlation energy obtained in V-QMC calculations. A detailed analysis of D-QMC time-step bias is made and is found to be at least linear with respect to the time-step. The D-QMC calculations determined the lithium cluster ionization potentials to be 0.1982(14) (0.1981), 0.1895(9) (0.1874(4)), 0.1530(34) (0.1599(73)), 0.1664(37) (0.1724(110)), 0.1613(43) (0.1675(110)) Hartrees for lithium clusters n = 1 through 5, respectively; in good agreement with experimental results shown in the brackets. Also, the binding energies per atom was computed to be 0.0177(8) (0.0203(12)), 0.0188(10) (0.0220(21)), 0.0247(8) (0.0310(12)), 0.0253(8) (0.0351(8)) Hartrees for lithium clusters n = 2 through 5, respectively. The lithium cluster one-electron density is shown to have charge concentrations corresponding to nonnuclear attractors. The overall shape of the electronic charge density also bears a remarkable similarity with the anisotropic harmonic oscillator model shape for the given number of valence electrons.

  3. The Forecasting of Labour Force Participation and the Unemployment Rate in Poland and Turkey Using Fuzzy Time Series Methods

    Directory of Open Access Journals (Sweden)

    Yolcu Ufuk

    2016-06-01

    Full Text Available Fuzzy time series methods based on the fuzzy set theory proposed by Zadeh (1965 was first introduced by Song and Chissom (1993. Since fuzzy time series methods do not have the assumptions that traditional time series do and have effective forecasting performance, the interest on fuzzy time series approaches is increasing rapidly. Fuzzy time series methods have been used in almost all areas, such as environmental science, economy and finance. The concepts of labour force participation and unemployment have great importance in terms of both the economy and sociology of countries. For this reason there are many studies on their forecasting. In this study, we aim to forecast the labour force participation and unemployment rate in Poland and Turkey using different fuzzy time series methods.

  4. Optimal Model for Velocity Strength Training Methods for Boy Sprinters Base on Fuzzy Matrix

    Directory of Open Access Journals (Sweden)

    Juntao Sun

    2013-04-01

    Full Text Available With literature consultation, Delphi method, fuzzy mathematics, experimental method and mathematical statistics method, from multi viewpoints, this study analyzes the velocity strength quality of boy two-level sprinters in quality and quantity. The result shows, with fuzzy mathematics, we can judge the relative degree between velocity strength and various methods, with quantitative disposal to analyze quantitatively, which has certain theoretic significance; according to analysis of fuzzy relation and corresponding relation, build the classification figure for velocity strength training method for boy sprinters; relative data proof, the optimal organization of different training methods can outstand the training specialization and save time and energy, so as to supplement the special training theories, to provide theoretic references and practical instructions for most coaches’ training processes, to improve the efficiency.

  5. A new method based on Dempster-Shafer theory and fuzzy c-means for brain MRI segmentation

    Science.gov (United States)

    Liu, Jie; Lu, Xi; Li, Yunpeng; Chen, Xiaowu; Deng, Yong

    2015-10-01

    In this paper, a new method is proposed to decrease sensitiveness to motion noise and uncertainty in magnetic resonance imaging (MRI) segmentation especially when only one brain image is available. The method is approached with considering spatial neighborhood information by fusing the information of pixels with their neighbors with Dempster-Shafer (DS) theory. The basic probability assignment (BPA) of each single hypothesis is obtained from the membership function of applying fuzzy c-means (FCM) clustering to the gray levels of the MRI. Then multiple hypotheses are generated according to the single hypothesis. Then we update the objective pixel’s BPA by fusing the BPA of the objective pixel and those of its neighbors to get the final result. Some examples in MRI segmentation are demonstrated at the end of the paper, in which our method is compared with some previous methods. The results show that the proposed method is more effective than other methods in motion-blurred MRI segmentation.

  6. Interactive exploration of uncertainty in fuzzy classifications by isosurface visualization of class clusters

    NARCIS (Netherlands)

    Lucieer, A.; Veen, L.E.

    2009-01-01

    Uncertainty and vagueness are important concepts when dealing with transition zones between vegetation communities or land-cover classes. In this study, classification uncertainty is quantified by applying a supervised fuzzy classification algorithm. New visualization techniques are proposed and pre

  7. Solving the Fully Fuzzy Bilevel Linear Programming Problem through Deviation Degree Measures and a Ranking Function Method

    Directory of Open Access Journals (Sweden)

    Aihong Ren

    2016-01-01

    Full Text Available This paper is concerned with a class of fully fuzzy bilevel linear programming problems where all the coefficients and decision variables of both objective functions and the constraints are fuzzy numbers. A new approach based on deviation degree measures and a ranking function method is proposed to solve these problems. We first introduce concepts of the feasible region and the fuzzy optimal solution of a fully fuzzy bilevel linear programming problem. In order to obtain a fuzzy optimal solution of the problem, we apply deviation degree measures to deal with the fuzzy constraints and use a ranking function method of fuzzy numbers to rank the upper and lower level fuzzy objective functions. Then the fully fuzzy bilevel linear programming problem can be transformed into a deterministic bilevel programming problem. Considering the overall balance between improving objective function values and decreasing allowed deviation degrees, the computational procedure for finding a fuzzy optimal solution is proposed. Finally, a numerical example is provided to illustrate the proposed approach. The results indicate that the proposed approach gives a better optimal solution in comparison with the existing method.

  8. SVC control method to improve the stability of power systems applying fuzzy control. Fuzzy seigyo wo riyoshita SVC ni yoru denryoku keito no anteika seigyoho

    Energy Technology Data Exchange (ETDEWEB)

    Uezato, K.; Senju, T.; Shiroma, T. (University of the Ryukyus, Okinawa (Japan))

    1994-03-01

    The SVC (static var compensator) control method featured by fuzzy control is proposed to improve the stabilization of power systems. The method is applicable to a simple single-machine infinite bus system, and SVC is allocated at the center of a transmission line to keep the line terminal voltage constant. The SVC controller is composed of the PI controller to keep the terminal voltage constant and the fuzzy controller-1 parallel to the PI controller for determining SVC admittances to suppress system fluctuation. The fuzzy controller-2 switches control between stabilizing control during system fluctuation and constant voltage control in normal operation. The fuzzy rules are remarkably simple because those are constructed qualitatively on the basis of sliding mode control. System fluctuation can be also reduced rapidly by using not only the terminal information such as terminal voltage and power flow on an interconnection line but also the generator information such as load angle and slip. 10 refs., 24 figs., 7 tabs.

  9. Extension of Axiomatic Design Method for Fuzzy Linguistic Multiple Criteria Group Decision Making with Incomplete Weight Information

    OpenAIRE

    Ming Li

    2012-01-01

    Axiomatic design (AD) provides a framework to describe design objects and a set of axioms to evaluate relations between intended functions and means by which they are achieved. It has been extended to evaluate alternatives in engineering under fuzzy environment. With respect to multiple criteria group decision making (MCDM) with incomplete weight information under fuzzy linguistic environment, a new method is proposed. In the method, the fuzzy axiomatic design based on triangle representation...

  10. A Fuzzy Obstacle Avoidance Controller Using a Lookup-Table Sharing Method and Its Applications for Mobile Robots

    Directory of Open Access Journals (Sweden)

    Jinwook Kim

    2011-11-01

    Full Text Available A Lookup‐Table (LUT based design enhances the processing speed of a fuzzy obstacle avoidance controller by reducing the operation time. Also, a LUT sharing method provides efficient ways of reducing the LUT memory size. In order to share the LUT which is used for a fuzzy obstacle avoidance controller, an idea of using a basis function is developed. As applications of the shared LUT‐based fuzzy controller, a laser‐sensor‐based fuzzy controller and an ultrasonic‐sensor‐based fuzzy controller are introduced in this paper. This paper suggests a LUT sharing method that reduces the LUT buffer size without a significant degradation of the performance. The LUT sharing method makes the buffer size independent of the fuzzy system

  11. A novel computing three-dimensional differential transform method for solving fuzzy partial differential equations

    Directory of Open Access Journals (Sweden)

    Farshid Mirzaee

    2016-06-01

    Full Text Available In this paper, we introduce three-dimensional fuzzy differential transform method and we utilize it to solve fuzzy partial differential equations. This technique is a successful method because of reducing such problems to solve a system of algebraic equations; so, the problem can be solved directly. A considerable advantage of this method is to obtain the analytical solutions if the equation has an exact solution that is a polynomial function. Numerical examples are included to demonstrate the validity and applicability of the method.

  12. Semi analytical solution of second order fuzzy Riccati equation by homotopy perturbation method

    Science.gov (United States)

    Jameel, A. F.; Ismail, Ahmad Izani Md

    2014-07-01

    In this work, the Homotopy Perturbation Method (HPM) is formulated to find a semi-analytical solution of the Fuzzy Initial Value Problem (FIVP) involving nonlinear second order Riccati equation. This method is based upon homotopy perturbation theory. This method allows for the solution of the differential equation to be calculated in the form of an infinite series in which the components can be easily calculated. The effectiveness of the algorithm is demonstrated by solving nonlinear second order fuzzy Riccati equation. The results indicate that the method is very effective and simple to apply.

  13. A new method for image segmentation based on Fuzzy C-means algorithm on pixonal images formed by bilateral filtering

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Sharifzadeh, Sara

    2013-01-01

    In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over s...... the hierarchical clustering method (Fuzzy C-means algorithm). The experimental results show that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-based image segmentation techniques.......In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over...

  14. Optimization of a Fuzzy-Logic-Control-Based Five-Stage Battery Charger Using a Fuzzy-Based Taguchi Method

    Directory of Open Access Journals (Sweden)

    Yeh-Hsiang Ho

    2013-07-01

    Full Text Available Lithium ion (Li-ion batteries have been widely used in various kinds of applications, including consumer electronics, green energy systems and electrical vehicles. Since the charging method has a significant influence on the performance and lifetime of Li-ion batteries, an intelligent charging algorithm which can properly determine the charging current is essential. In this study, a fuzzy-logic-control-based (FLC-based five-stage Li-ion battery charger is proposed. The proposed charger takes the temperature rise and the gradient of temperature rise of battery into account, and adjusts the charging current accordingly. To further improve the performance of the proposed FLC, the fuzzy-based Taguchi method is utilized to determine the optimal output membership functions (MFs. Comparing with the conventional constant current-constant voltage (CC-CV method, the charging time, charging efficiency, average temperature rise and the obtained cycle life of the Li-ion battery are improved by about 58.3%, 1.65%, 26.7% and 59.3%, respectively.

  15. Fuzzy logic and its application in football team ranking.

    Science.gov (United States)

    Zeng, Wenyi; Li, Junhong

    2014-01-01

    Fuzzy set theory and fuzzy logic are a highly suitable and applicable basis for developing knowledge-based systems in physical education for tasks such as the selection for athletes, the evaluation for different training approaches, the team ranking, and the real-time monitoring of sports data. In this paper, we use fuzzy set theory and apply fuzzy clustering analysis in football team ranking. Based on some certain rules, we propose four parameters to calculate fuzzy similar matrix, obtain fuzzy equivalence matrix and the ranking result for our numerical example, T 7, T 3, T 1, T 9, T 10, T 8, T 11, T 12, T 2, T 6, T 5, T 4, and investigate four parameters sensitivity analysis. The study shows that our fuzzy logic method is reliable and stable when the parameters change in certain range.

  16. Water quality assessment in Qu River based on fuzzy water pollution index method.

    Science.gov (United States)

    Li, Ranran; Zou, Zhihong; An, Yan

    2016-12-01

    A fuzzy improved water pollution index was proposed based on fuzzy inference system and water pollution index. This method can not only give a comprehensive water quality rank, but also describe the water quality situation with a quantitative value, which is convenient for the water quality comparison between the same ranks. This proposed method is used to assess water quality of Qu River in Sichuan, China. Data used in the assessment were collected from four monitoring stations from 2006 to 2010. The assessment results show that Qu River water quality presents a downward trend and the overall water quality in 2010 is the worst. The spatial variation indicates that water quality of Nanbashequ section is the pessimal. For the sake of comparison, fuzzy comprehensive evaluation and grey relational method were also employed to assess water quality of Qu River. The comparisons of these three approaches' assessment results show that the proposed method is reliable.

  17. Fuzzy physical programming for Space Manoeuvre Vehicles trajectory optimization based on hp-adaptive pseudospectral method

    Science.gov (United States)

    Chai, Runqi; Savvaris, Al; Tsourdos, Antonios

    2016-06-01

    In this paper, a fuzzy physical programming (FPP) method has been introduced for solving multi-objective Space Manoeuvre Vehicles (SMV) skip trajectory optimization problem based on hp-adaptive pseudospectral methods. The dynamic model of SMV is elaborated and then, by employing hp-adaptive pseudospectral methods, the problem has been transformed to nonlinear programming (NLP) problem. According to the mission requirements, the solutions were calculated for each single-objective scenario. To get a compromised solution for each target, the fuzzy physical programming (FPP) model is proposed. The preference function is established with considering the fuzzy factor of the system such that a proper compromised trajectory can be acquired. In addition, the NSGA-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the FPP solution. Simulation results indicate that the proposed method is effective and feasible in terms of dealing with the multi-objective skip trajectory optimization for the SMV.

  18. Fuzzy PID Control Method for Internet-based Tele-operation Manipulators System

    Directory of Open Access Journals (Sweden)

    Wei Gao

    2013-11-01

    Full Text Available Trajectory tracking control problem for internet-based tele-operation system is researched in this paper. The control structure of master and slave tele-operation manipulators adapts bilateral servo control architecture with force deviation feedback. The simulation model of three degrees of freedom (3-DOF manipulator is presented. In order to ensure the synchronization of positions of the master and slave manipulators, a fuzzy PID control method is proposed. This control algorithm is to adjust the three parameters of PID controller online by fuzzy control method. The contrast simulation experiments of PID and fuzzy PID control methods show that the proposed control method can effectively improve the force and position tracking performance and reduce time delay.

  19. PERFORMANCE OF SELECTED AGGLOMERATIVE HIERARCHICAL CLUSTERING METHODS

    Directory of Open Access Journals (Sweden)

    Nusa Erman

    2015-01-01

    Full Text Available A broad variety of different methods of agglomerative hierarchical clustering brings along problems how to choose the most appropriate method for the given data. It is well known that some methods outperform others if the analysed data have a specific structure. In the presented study we have observed the behaviour of the centroid, the median (Gower median method, and the average method (unweighted pair-group method with arithmetic mean – UPGMA; average linkage between groups. We have compared them with mostly used methods of hierarchical clustering: the minimum (single linkage clustering, the maximum (complete linkage clustering, the Ward, and the McQuitty (groups method average, weighted pair-group method using arithmetic averages - WPGMA methods. We have applied the comparison of these methods on spherical, ellipsoid, umbrella-like, “core-and-sphere”, ring-like and intertwined three-dimensional data structures. To generate the data and execute the analysis, we have used R statistical software. Results show that all seven methods are successful in finding compact, ball-shaped or ellipsoid structures when they are enough separated. Conversely, all methods except the minimum perform poor on non-homogenous, irregular and elongated ones. Especially challenging is a circular double helix structure; it is being correctly revealed only by the minimum method. We can also confirm formerly published results of other simulation studies, which usually favour average method (besides Ward method in cases when data is assumed to be fairly compact and well separated.

  20. Fuzzy Pattern Recognition System for Detection of Alga Distribution

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    To realize the on-line measurement and make analysis on the density of algae and their cluster distribution, the fluorescent detection and fuzzy pattern recognition techniques are used. The principle of fluorescent fiber-optic detection is given as well as the method of fuzzy feature extraction using a class of neural network.

  1. A fuzzy-logic based MPPT method for stand-alone wind turbine system

    Directory of Open Access Journals (Sweden)

    Huynh Quang Minh

    2014-09-01

    Full Text Available In this paper, a fuzzy-logic based maximum power point tracking (MPPT method for a standalone wind turbine system is proposed. Hill climb searching (HCS method is usedto achieve the MPPT of thepermanent magnet synchronous generator (PMSG driven wind turbine system. Simulation results will show the effectiveness of the proposed method in various operating conditions.

  2. Application of Homotopy Perturbation Method for Fuzzy Linear Systems and Comparison with Adomian’s Decomposition Method

    Directory of Open Access Journals (Sweden)

    H. Saberi Najafi

    2013-01-01

    Full Text Available We present an efficient numerical algorithm for solution of the fuzzy linear systems (FLS based on He’s homotopy perturbation method (HPM. Moreover, the convergence properties of the proposed method have been analyzed and also comparisons are made between Adomian’s decomposition method (ADM and the proposed method. The results reveal that our method is effective and simple.

  3. A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China

    Science.gov (United States)

    Hong, Haoyuan; Ilia, Ioanna; Tsangaratos, Paraskevas; Chen, Wei; Xu, Chong

    2017-08-01

    The present study proposed a hybrid fuzzy weight of evidence model for constructing a landslide susceptibility map in the Wuyuan area, China, where disastrous landslide events have occurred. The model combines the knowledge of experts obtained through a fuzzy logic approach and a hybrid weight of evidence method. The estimated knowledge-based fuzzy membership value of each environmental variable is combined with data-based conditional probabilities to derive fuzzy posterior probabilities and landslide susceptibility. The developed model was compared with a landslide susceptibility map produced using the data-driven weight of evidence method, based on 510 landslide and non-landslide sites. The sites were identified by analyzing airborne imagery, field investigation and previous studies. Landside susceptibility for these sites was analyzed using 10 geo-environmental variables: slope, aspect, lithology, soil, rainfall, plan curvature, the normalized difference vegetation index, distance to roads, distance to rivers and distance to faults. The resultant hybrid fuzzy weight of evidence method showed high predictive power, with the area under the success and predictive curves being 0.770 and 0.746, respectively. Additional analyses showed that the developed model could work effectively even with limited data.

  4. The Interval-Valued Triangular Fuzzy Soft Set and Its Method of Dynamic Decision Making

    Directory of Open Access Journals (Sweden)

    Xiaoguo Chen

    2014-01-01

    Full Text Available A concept of interval-valued triangular fuzzy soft set is presented, and some operations of “AND,” “OR,” intersection, union and complement, and so forth are defined. Then some relative properties are discussed and several conclusions are drawn. A dynamic decision making model is built based on the definition of interval-valued triangular fuzzy soft set, in which period weight is determined by the exponential decay method. The arithmetic weighted average operator of interval-valued triangular fuzzy soft set is given by the aggregating thought, thereby aggregating interval-valued triangular fuzzy soft sets of different time-series into a collective interval-valued triangular fuzzy soft set. The formulas of selection and decision values of different objects are given; therefore the optimal decision making is achieved according to the decision values. Finally, the steps of this method are concluded, and one example is given to explain the application of the method.

  5. Research on conflict resolution of collaborative design with fuzzy case-based reasoning method

    Institute of Scientific and Technical Information of China (English)

    HOU Jun-ming; SU Chong; LIANG Shuang; WANG Wan-shan

    2009-01-01

    Collaborative design is a new style for modern mechanical design to meet the requirement of increasing competition. Designers of different places complete the same work, but the conflict appears in the process of design which may interface the design. Case-based reasoning (CBR) method is applied to the problem of conflict resolution, which is in the artificial intelligence field. However, due to the uncertainties in knowledge representation, attribute description, and similarity measures of CBR, it is very difficult to find the similar cases from case database. A fuzzy CBR method was proposed to solve the problem of conflict resolution in collaborative design. The process of fuzzy CBR was introduced. Based on the feature attributes and their relative weights determined by a fuzzy technique, a fuzzy CBR retrieving mechanism was developed to retrieve conflict resolution cases that tend to enhance the functions of the database. By indexing, calculating the weight and defuzzicating of the cases, the case similarity can be obtained. Then the case consistency was measured to keep the right result. Finally, the fuzzy CBR method for conflict resolution was demonstrated by means of a case study. The prototype system based on web is developed to illustrate the methodology.

  6. A fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Koffka Khan

    2012-12-01

    Full Text Available Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results.

  7. Fuzzy Boundary and Fuzzy Semiboundary

    OpenAIRE

    Athar, M.; Ahmad, B.

    2008-01-01

    We present several properties of fuzzy boundary and fuzzy semiboundary which have been supported by examples. Properties of fuzzy semi-interior, fuzzy semiclosure, fuzzy boundary, and fuzzy semiboundary have been obtained in product-related spaces. We give necessary conditions for fuzzy continuous (resp., fuzzy semicontinuous, fuzzy irresolute) functions. Moreover, fuzzy continuous (resp., fuzzy semicontinuous, fuzzy irresolute) functions have been characterized via fuzzy-derived (resp., fuzz...

  8. Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation.

    Science.gov (United States)

    Mekhmoukh, Abdenour; Mokrani, Karim

    2015-11-01

    In this paper, a new image segmentation method based on Particle Swarm Optimization (PSO) and outlier rejection combined with level set is proposed. A traditional approach to the segmentation of Magnetic Resonance (MR) images is the Fuzzy C-Means (FCM) clustering algorithm. The membership function of this conventional algorithm is sensitive to the outlier and does not integrate the spatial information in the image. The algorithm is very sensitive to noise and in-homogeneities in the image, moreover, it depends on cluster centers initialization. To improve the outlier rejection and to reduce the noise sensitivity of conventional FCM clustering algorithm, a novel extended FCM algorithm for image segmentation is presented. In general, in the FCM algorithm the initial cluster centers are chosen randomly, with the help of PSO algorithm the clusters centers are chosen optimally. Our algorithm takes also into consideration the spatial neighborhood information. These a priori are used in the cost function to be optimized. For MR images, the resulting fuzzy clustering is used to set the initial level set contour. The results confirm the effectiveness of the proposed algorithm.

  9. A peculiar object in M51: fuzzy star cluster or a background galaxy?

    CERN Document Server

    Scheepmaker, R A; Larsen, S S; Anders, P

    2007-01-01

    Aims: We study a peculiar object with a projected position close to the nucleus of M51. It is unusually large for a star cluster in M51 and we therefore investigate the three most likely options to explain this object: (a) a background galaxy, (b) a cluster in the disk of M51 and (c) a cluster in M51, but in front of the disk. Methods: We use HST/ACS and HST/NICMOS broad-band photometry to study the properties of this object. Assuming the object is a star cluster, we fit the metallicity, age, mass and extinction using simple stellar population models. Assuming the object is a background galaxy, we estimate the extinction from the colour of the background around the object. We study the structural parameters of the object by fitting the spatial profile with analytical models. Results: We find de-reddened colours of the object which are bluer than expected for a typical elliptical galaxy, and the central surface brightness is brighter than the typical surface brightness of a disc galaxy. It is therefore not lik...

  10. A new model for virtual machine migration in virtualized cluster server based on Fuzzy Decision Making

    CERN Document Server

    Tarighi, M; Sharifian, S

    2010-01-01

    In this paper, we show that performance of the virtualized cluster servers could be improved through intelligent decision over migration time of Virtual Machines across heterogeneous physical nodes of a cluster server. The cluster serves a variety range of services from Web Service to File Service. Some of them are CPU-Intensive while others are RAM-Intensive and so on. Virtualization has many advantages such as less hardware cost, cooling cost, more manageability. One of the key benefits is better load balancing by using of VM migration between hosts. To migrate, we must know which virtual machine needs to be migrated and when this relocation has to be done and, moreover, which host must be destined. To relocate VMs from overloaded servers to underloaded ones, we need to sort nodes from the highest volume to the lowest. There are some models to finding the most overloaded node, but they have some shortcomings. The focus of this paper is to present a new method to migrate VMs between cluster nodes using TOPSI...

  11. 2-D minimum fuzzy entropy method of image thresholding based on genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the characteristic relationship between the gray level of each pixel and the average value of its neighborhood. When the threshold is not located at the obvious and deep valley ofthe histgram, genetic algorithm is devoted to the problem of selecting the appropriate threshold value. The experimental results indicate that the proposed method has good performance.

  12. Evaluation of Cloud Services: A Fuzzy Multi-Criteria Group Decision Making Method

    Directory of Open Access Journals (Sweden)

    Santoso Wibowo

    2016-12-01

    Full Text Available This paper presents a fuzzy multi-criteria group decision making method for evaluating the performance of Cloud services in an uncertain environment. Intuitionistic fuzzy numbers are used to better model the subjectivity and imprecision in the performance evaluation process. An effective algorithm is developed based on the technique for order preference by similarity to the ideal solution and the Choquet integral operator for adequately solving the performance evaluation problem. An example is presented for demonstrating the applicability of the proposed method for solving the multi-criteria group decision making problem in real situations.

  13. Quadrature Rules and Iterative Method for Numerical Solution of Two-Dimensional Fuzzy Integral Equations

    Directory of Open Access Journals (Sweden)

    S. M. Sadatrasoul

    2014-01-01

    Full Text Available We introduce some generalized quadrature rules to approximate two-dimensional, Henstock integral of fuzzy-number-valued functions. We also give error bounds for mappings of bounded variation in terms of uniform modulus of continuity. Moreover, we propose an iterative procedure based on quadrature formula to solve two-dimensional linear fuzzy Fredholm integral equations of the second kind (2DFFLIE2, and we present the error estimation of the proposed method. Finally, some numerical experiments confirm the theoretical results and illustrate the accuracy of the method.

  14. Document Clustering using Sequential Information Bottleneck Method

    CERN Document Server

    Gayathri, P J; Punithavalli, M

    2010-01-01

    This paper illustrates the Principal Direction Divisive Partitioning (PDDP) algorithm and describes its drawbacks and introduces a combinatorial framework of the Principal Direction Divisive Partitioning (PDDP) algorithm, then describes the simplified version of the EM algorithm called the spherical Gaussian EM (sGEM) algorithm and Information Bottleneck method (IB) is a technique for finding accuracy, complexity and time space. The PDDP algorithm recursively splits the data samples into two sub clusters using the hyper plane normal to the principal direction derived from the covariance matrix, which is the central logic of the algorithm. However, the PDDP algorithm can yield poor results, especially when clusters are not well separated from one another. To improve the quality of the clustering results problem, it is resolved by reallocating new cluster membership using the IB algorithm with different settings. IB Method gives accuracy but time consumption is more. Furthermore, based on the theoretical backgr...

  15. Method study on fuzzy-PID adaptive control of electric-hydraulic hitch system

    Science.gov (United States)

    Li, Mingsheng; Wang, Liubu; Liu, Jian; Ye, Jin

    2017-03-01

    In this paper, fuzzy-PID adaptive control method is applied to the control of tractor electric-hydraulic hitch system. According to the characteristics of the system, a fuzzy-PID adaptive controller is designed and the electric-hydraulic hitch system model is established. Traction control and position control performance simulation are carried out with the common PID control method. A field test rig was set up to test the electric-hydraulic hitch system. The test results showed that, after the fuzzy-PID adaptive control is adopted, when the tillage depth steps from 0.1m to 0.3m, the system transition process time is 4s, without overshoot, and when the tractive force steps from 3000N to 7000N, the system transition process time is 5s, the system overshoot is 25%.

  16. Hesitant Fuzzy Linguistic Multicriteria Decision-Making Method Based on Generalized Prioritized Aggregation Operator

    Directory of Open Access Journals (Sweden)

    Jia-ting Wu

    2014-01-01

    Full Text Available Based on linguistic term sets and hesitant fuzzy sets, the concept of hesitant fuzzy linguistic sets was introduced. The focus of this paper is the multicriteria decision-making (MCDM problems in which the criteria are in different priority levels and the criteria values take the form of hesitant fuzzy linguistic numbers (HFLNs. A new approach to solving these problems is proposed, which is based on the generalized prioritized aggregation operator of HFLNs. Firstly, the new operations and comparison method for HFLNs are provided and some linguistic scale functions are applied. Subsequently, two prioritized aggregation operators and a generalized prioritized aggregation operator of HFLNs are developed and applied to MCDM problems. Finally, an illustrative example is given to illustrate the effectiveness and feasibility of the proposed method, which are then compared to the existing approach.

  17. Hesitant fuzzy linguistic multicriteria decision-making method based on generalized prioritized aggregation operator.

    Science.gov (United States)

    Wu, Jia-ting; Wang, Jian-qiang; Wang, Jing; Zhang, Hong-yu; Chen, Xiao-hong

    2014-01-01

    Based on linguistic term sets and hesitant fuzzy sets, the concept of hesitant fuzzy linguistic sets was introduced. The focus of this paper is the multicriteria decision-making (MCDM) problems in which the criteria are in different priority levels and the criteria values take the form of hesitant fuzzy linguistic numbers (HFLNs). A new approach to solving these problems is proposed, which is based on the generalized prioritized aggregation operator of HFLNs. Firstly, the new operations and comparison method for HFLNs are provided and some linguistic scale functions are applied. Subsequently, two prioritized aggregation operators and a generalized prioritized aggregation operator of HFLNs are developed and applied to MCDM problems. Finally, an illustrative example is given to illustrate the effectiveness and feasibility of the proposed method, which are then compared to the existing approach.

  18. A Water Quality Monitoring Method Based on Fuzzy Comprehensive Evaluation in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jian Shu

    2012-01-01

    Full Text Available A novel water quality monitoring method named WQMMFCE is proposed based on fuzzy comprehensive evaluation model to solve the issue of high energy consumption caused by centralized approach under the background of water quality monitoring. The weights of all factors which are necessary for fuzzy comprehensive evaluation can be obtained firstly by using the binary expert evaluation. The information needs to transmit is decided according to water quality grade which is quantitatively analyzed directly by using fuzzy comprehensive evaluation rather than transmitting large amount of raw data to the monitoring center. Simulation result shows that the method has a great advantage over the centralized one on energy saving and can prolong the lifetime of the network.

  19. PSO type-reduction method for geometric interval type-2 fuzzy logic systems

    Institute of Scientific and Technical Information of China (English)

    ZHAO Xian-zhang; GAO Yi-bo; ZENG Jun-fang; YANG Yi-ping

    2008-01-01

    In a special case of type-2 fuzzy logic systems (FLS), i.e. geometric interval type-2 fuzzy logic sys-tems (GIT-2FLS), the crisp output is obtained by computing the geometric center of footprint of uncertainty (FOU) without type-reduction, but the defuzzifying method acts against the corner concepts of type-2 fuzzy sets in some cases. In this paper, a PSO type-reduction method for GIT-2FLS based on the particle swarm optimiza-tion (PSO) algorithm is presented. With the PSO type-reduction, the inference principle of geometric interval FLS operating on the continuous domain is consistent with that of traditional interval type-2 FLS operating on the discrete domain. With comparative experiments, it is proved that the PSO type-reduction exhibits good perform-ance, and is a satisfactory complement for the theory of GIT-2FLS.

  20. Fuzzy recurrence plots

    Science.gov (United States)

    Pham, T. D.

    2016-12-01

    Recurrence plots display binary texture of time series from dynamical systems with single dots and line structures. Using fuzzy recurrence plots, recurrences of the phase-space states can be visualized as grayscale texture, which is more informative for pattern analysis. The proposed method replaces the crucial similarity threshold required by symmetrical recurrence plots with the number of cluster centers, where the estimate of the latter parameter is less critical than the estimate of the former.

  1. Approximate Analytic and Numerical Solutions to Lane-Emden Equation via Fuzzy Modeling Method

    Directory of Open Access Journals (Sweden)

    De-Gang Wang

    2012-01-01

    Full Text Available A novel algorithm, called variable weight fuzzy marginal linearization (VWFML method, is proposed. This method can supply approximate analytic and numerical solutions to Lane-Emden equations. And it is easy to be implemented and extended for solving other nonlinear differential equations. Numerical examples are included to demonstrate the validity and applicability of the developed technique.

  2. A Comparison of Neural Networks and Fuzzy Logic Methods for Process Modeling

    Science.gov (United States)

    Cios, Krzysztof J.; Sala, Dorel M.; Berke, Laszlo

    1996-01-01

    The goal of this work was to analyze the potential of neural networks and fuzzy logic methods to develop approximate response surfaces as process modeling, that is for mapping of input into output. Structural response was chosen as an example. Each of the many methods surveyed are explained and the results are presented. Future research directions are also discussed.

  3. Research on Clothing Retail Store Location in Shanghai Business Streets With Fuzzy Comprehensive Evaluation Method

    Institute of Scientific and Technical Information of China (English)

    CANG Ping

    2005-01-01

    Based on the factors influencing location selection, this paper set up an evaluation system and got evaluation results of the related indexes in four main business centers in Shanghai with fuzzy comprehensive evaluation method. In the mean time, this method can be applied in the evaluation of retail store location selection.

  4. Evaluate E-loyalty of sales website: a Fuzzy mathematics method

    Science.gov (United States)

    Yi, Ying; Liu, Zhen-Yu; Xiong, Ying-Zi

    The study about online consumer loyalty is limited, but how to evaluate the customers' E-loyalty to a sales website is always a noticeable question. By using some methods of fuzzy mathematics, we provide a more accurate way to evaluate E-loyalty of sales website. Moreover, this method can differentiate level and degree of each factor that influences E-loyalty.

  5. Fuzzy methods in decision making process - A particular approach in manufacturing systems

    Science.gov (United States)

    Coroiu, A. M.

    2015-11-01

    We are living in a competitive environment, so we can see and understand that the most of manufacturing firms do the best in order to accomplish meeting demand, increasing quality, decreasing costs, and delivery rate. In present a stake point of interest is represented by the development of fuzzy technology. A particular approach for this is represented through the development of methodologies to enhance the ability to managed complicated optimization and decision making aspects involving non-probabilistic uncertainty with the reason to understand, development, and practice the fuzzy technologies to be used in fields such as economic, engineering, management, and societal problems. Fuzzy analysis represents a method for solving problems which are related to uncertainty and vagueness; it is used in multiple areas, such as engineering and has applications in decision making problems, planning and production. As a definition for decision making process we can use the next one: result of mental processes based upon cognitive process with a main role in the selection of a course of action among several alternatives. Every process of decision making can be represented as a result of a final choice and the output can be represented as an action or as an opinion of choice. Different types of uncertainty can be discovered in a wide variety of optimization and decision making problems related to planning and operation of power systems and subsystems. The mixture of the uncertainty factor in the construction of different models serves for increasing their adequacy and, as a result, the reliability and factual efficiency of decisions based on their analysis. Another definition of decision making process which came to illustrate and sustain the necessity of using fuzzy method: the decision making is an approach of choosing a strategy among many different projects in order to achieve some purposes and is formulated as three different models: high risk decision, usual risk

  6. Low Carbon Supply Chain’s Performance Evaluation Based on Entropy Method and Fuzzy Comprehensive Evaluation Method

    Directory of Open Access Journals (Sweden)

    Xu Xu

    2013-06-01

    Full Text Available This study constructed a performance evaluation index system of low carbon supply chain from the economic, resources and environment. This index system highlights the environmental value orientation and green culture technology evaluation. On this basis, uses entropy value method to definite the index system of index weigh and uses the fuzzy comprehensive evaluation method to establish the evaluation model. It overcomes the respective faults of the entropy value method and fuzzy comprehensive evaluation method and makes low carbon supply chain performance evaluation more scientific and accurate. Finally, the model was verified analysis.

  7. Esophageal cancer prediction based on qualitative features using adaptive fuzzy reasoning method

    Directory of Open Access Journals (Sweden)

    Raed I. Hamed

    2015-04-01

    Full Text Available Esophageal cancer is one of the most common cancers world-wide and also the most common cause of cancer death. In this paper, we present an adaptive fuzzy reasoning algorithm for rule-based systems using fuzzy Petri nets (FPNs, where the fuzzy production rules are represented by FPN. We developed an adaptive fuzzy Petri net (AFPN reasoning algorithm as a prognostic system to predict the outcome for esophageal cancer based on the serum concentrations of C-reactive protein and albumin as a set of input variables. The system can perform fuzzy reasoning automatically to evaluate the degree of truth of the proposition representing the risk degree value with a weight value to be optimally tuned based on the observed data. In addition, the implementation process for esophageal cancer prediction is fuzzily deducted by the AFPN algorithm. Performance of the composite model is evaluated through a set of experiments. Simulations and experimental results demonstrate the effectiveness and performance of the proposed algorithms. A comparison of the predictive performance of AFPN models with other methods and the analysis of the curve showed the same results with an intuitive behavior of AFPN models.

  8. Situation Assessment Technology Based on Target Clustering and Fuzzy Matching%基于目标分群与模糊匹配的态势评估技术

    Institute of Scientific and Technical Information of China (English)

    刘秀文

    2012-01-01

    The situation assessment system is implemented using target clustering and fuzzy matching techniques. First, the development of data fusion and fusion models are introduced. Based on the JDL revised process model,the general technical framework, function modules and key techniques of the system are proposed. Then the process of target clustering is introduced, which includes target clustering, cluster splitting and merging, and the target clustering algorithm and fuzzy matching algorithm are presented. Besides, the methods for military organizational structure reasoning based on fuzzy matching technique are reviewed. In the end, the calculation result is given to prove the effectiveness of the target clustering algorithm.%采用目标分群和部队编制模糊匹配技术实现了态势评估系统。介绍了数据融合发展概况与融合模型,在数据融合修正模型的基础上,提出了态势评估总体技术框架、功能模块和关键技术。介绍了目标分群处理流程,包括目标分群、群的分裂与合并,并进一步阐述了目标分群算法与模糊匹配算法。介绍了基于模糊匹配技术实现军事体系单元假设推理的方法,给出目标分群计算结果,说明了算法的有效性。

  9. Use of multiple cluster analysis methods to explore the validity of a community outcomes concept map.

    Science.gov (United States)

    Orsi, Rebecca

    2017-02-01

    Concept mapping is now a commonly-used technique for articulating and evaluating programmatic outcomes. However, research regarding validity of knowledge and outcomes produced with concept mapping is sparse. The current study describes quantitative validity analyses using a concept mapping dataset. We sought to increase the validity of concept mapping evaluation results by running multiple cluster analysis methods and then using several metrics to choose from among solutions. We present four different clustering methods based on analyses using the R statistical software package: partitioning around medoids (PAM), fuzzy analysis (FANNY), agglomerative nesting (AGNES) and divisive analysis (DIANA). We then used the Dunn and Davies-Bouldin indices to assist in choosing a valid cluster solution for a concept mapping outcomes evaluation. We conclude that the validity of the outcomes map is high, based on the analyses described. Finally, we discuss areas for further concept mapping methods research.

  10. Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering

    Directory of Open Access Journals (Sweden)

    Sharma Animesh

    2007-01-01

    Full Text Available Abstract Background The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell tumor (SRBCT and thus often leads to misdiagnosis. Identification of biomarkers for distinguishing these cancers is a well studied problem. Existing methods typically evaluate each gene separately and do not take into account the nonlinear interaction between genes and the tools that are used to design the diagnostic prediction system. Consequently, more genes are usually identified as necessary for prediction. We propose a general scheme for finding a small set of biomarkers to design a diagnostic system for accurate classification of the cancer subgroups. We use multilayer networks with online gene selection ability and relational fuzzy clustering to identify a small set of biomarkers for accurate classification of the training and blind test cases of a well studied data set. Results Our method discerned just seven biomarkers that precisely categorized the four subgroups of cancer both in training and blind samples. For the same problem, others suggested 19–94 genes. These seven biomarkers include three novel genes (NAB2, LSP1 and EHD1 – not identified by others with distinct class-specific signatures and important role in cancer biology, including cellular proliferation, transendothelial migration and trafficking of MHC class antigens. Interestingly, NAB2 is downregulated in other tumors including Non-Hodgkin lymphoma and Neuroblastoma but we observed moderate to high upregulation in a few cases of Ewing sarcoma and Rabhdomyosarcoma, suggesting that NAB2 might be mutated in these tumors. These genes can discover the subgroups correctly with unsupervised learning, can differentiate non-SRBCT samples and they perform equally well with other machine learning tools including support vector machines. These biomarkers lead to four simple human interpretable

  11. A Reliable Visual Inspection Method for Vulnerability Assessment of Hyperstatic Structures Using Fuzzy Logic Analysis

    Directory of Open Access Journals (Sweden)

    Maria Valeria Piras

    2015-01-01

    Full Text Available Fuzzy logic applied to the visual inspection of existing buildings has been proposed in relation to simple structures. Isostatic structures are characterized by a unique and known collapse mechanism, which does not vary with geometry or load change. In this paper we apply fuzzy logic to visual inspection for complex structures such as hyperstatic ones in which the collapse mechanism depends not only on the geometry but also on the size and disposition of loads. The goal of this paper is to give relevant weight, in the fuzzy analysis, not only to the single expression of degradation, due to its localization within the element, but also to the structural element itself by assigning a different resistance to the various elements. The underlying aim of the proposed method is to manage, evaluate, and process all the information coming from visual inspections in order to realize a management information system for the evaluation of the safety level of even complex structures.

  12. A new fuzzy level set method for SAR image segmentation%基于模糊水平集的SAR图像分割方法

    Institute of Scientific and Technical Information of China (English)

    毛万峰; 张红; 张波; 王超

    2013-01-01

    We present a new method which integrates fuzzy c-means cluttering and region-based level set evolution for SAR image segmentation. Benefited by spatial fuzzy clustering, the initial level set segmentation approximates the component of interest. The controlling parameters are also estimated on the basis of the results of the spatial fuzzy clustering. The proposed method was evaluated on synthetic and real SAR images, and the results show that the new method is more robust, fast, and accurate in segmentation and does not need manual intervention.%提出一种SAR图像分割方法,即整合了模糊C均值聚类和基于区域水平集演化的分割方法.该方法通过模糊聚类的结果计算水平集演化的初始化条件及控制参数,从而克服了水平集演化依赖于初始化条件和控制参数且需要较多人工干预的缺陷,增强了方法的鲁棒性.模拟图像及真实SAR图像的实验表明,该方法在不需要人工干预的情况下,能够快速、准确地分割出感兴趣区域.

  13. Synchronization of Uncertain Time Delay Chaotic Systems using the Adaptive Fuzzy Method

    Institute of Scientific and Technical Information of China (English)

    关新平; 华长春

    2002-01-01

    We consider the synchronization problem of a class of first-order differential-delay chaotic systems. We utilize time-delay fuzzy logic systems to approximate continuous nonlinear time-delay functions, so that the precise mathematical model need not be known. Adopting the adaptive fuzzy control method, we construct a class of state feedback controllers which can render the closed-loop error systems to be asymptotically stable. We carry out simulations of synchronizing Mackey-Glass and logistic chaotic systems, and the results are reasonable.

  14. Multi-item fuzzy inventory problem with space constraint via geometric programming method

    Directory of Open Access Journals (Sweden)

    Mandal Kumar Nirmal

    2006-01-01

    Full Text Available In this paper, a multi-item inventory model with space constraint is developed in both crisp and fuzzy environment. A profit maximization inventory model is proposed here to determine the optimal values of demands and order levels of a product. Selling price and unit price are assumed to be demand-dependent and holding and set-up costs sock dependent. Total profit and warehouse space are considered to be vague and imprecise. The impreciseness in the above objective and constraint goals has been expressed by fuzzy linear membership functions. The problem is then solved using modified geometric programming method. Sensitivity analysis is also presented here.

  15. Estimación de Estados Funcionales en Procesos Complejos con Base en Agrupamiento Difuso Estimation of Functional States in Complex Processes Based on Fuzzy Clustering

    Directory of Open Access Journals (Sweden)

    Henry O Sarmiento

    2013-01-01

    Full Text Available Este artículo presenta una metodología para predecir estados funcionales en procesos complejos a partir de la estimación de grados de pertenencia difusos. La propuesta integra una medida estática como es el resultado de un clasificador difuso entrenado con los datos históricos del proceso y un algoritmo de estimación basado en la teoría de Markov para eventos discretos. La propuesta, que puede ser integrada a un sistema de monitoreo de sistemas complejos, comprende dos etapas: una etapa de entrenamiento fuera de línea para definir el clasificador difuso y el estimador; y una etapa en línea donde se realizan la clasificación de la situación actual del proceso y la estimación del estado funcional para el siguiente tiempo de muestreo. La propuesta desarrollada para la estimación de estados funcionales permite utilizar cualquier método de agrupamiento difuso que suministre la información base que requiere la metodología. La metodología fue probada con éxito en un sistema de monitoreo para una línea de transmisión de energía y en el monitoreo de un sistema de caldera.This paper presents a methodology to predict functional states in complex processes from the estimation of fuzzy membership degrees. The proposal integrates a static measure, such as the result of a fuzzy classifier trained with historical process data, and an estimation algorithm based on Markov theory for discrete events. The proposal, which can be integrated to the monitoring of complex systems, provides two stages: an off-line training stage to define the fuzzy classifier and the estimator; and an online stage where the classification of the current process situation and the estimation of the next functional state are performed. The proposal for the estimation of functional states allows using any fuzzy clustering method that provides the information required by the methodology. The proposed methodology was successfully tested on a monitoring system for a power

  16. A comparison of fuzzy logic and cluster renewal approaches for heat transfer modeling in a 1296 t/h CFB boiler with low level of flue gas recirculation

    Science.gov (United States)

    Błaszczuk, Artur; Krzywański, Jarosław

    2017-03-01

    The interrelation between fuzzy logic and cluster renewal approaches for heat transfer modeling in a circulating fluidized bed (CFB) has been established based on a local furnace data. The furnace data have been measured in a 1296 t/h CFB boiler with low level of flue gas recirculation. In the present study, the bed temperature and suspension density were treated as experimental variables along the furnace height. The measured bed temperature and suspension density were varied in the range of 1131-1156 K and 1.93-6.32 kg/m3, respectively. Using the heat transfer coefficient for commercial CFB combustor, two empirical heat transfer correlation were developed in terms of important operating parameters including bed temperature and also suspension density. The fuzzy logic results were found to be in good agreement with the corresponding experimental heat transfer data obtained based on cluster renewal approach. The predicted bed-to-wall heat transfer coefficient covered a range of 109-241 W/(m2K) and 111-240 W/(m2K), for fuzzy logic and cluster renewal approach respectively. The divergence in calculated heat flux recovery along the furnace height between fuzzy logic and cluster renewal approach did not exceeded ±2%.

  17. Fuzzy-logic based strategy for validation of multiplex methods: example with qualitative GMO assays.

    Science.gov (United States)

    Bellocchi, Gianni; Bertholet, Vincent; Hamels, Sandrine; Moens, W; Remacle, José; Van den Eede, Guy

    2010-02-01

    This paper illustrates the advantages that a fuzzy-based aggregation method could bring into the validation of a multiplex method for GMO detection (DualChip GMO kit, Eppendorf). Guidelines for validation of chemical, bio-chemical, pharmaceutical and genetic methods have been developed and ad hoc validation statistics are available and routinely used, for in-house and inter-laboratory testing, and decision-making. Fuzzy logic allows summarising the information obtained by independent validation statistics into one synthetic indicator of overall method performance. The microarray technology, introduced for simultaneous identification of multiple GMOs, poses specific validation issues (patterns of performance for a variety of GMOs at different concentrations). A fuzzy-based indicator for overall evaluation is illustrated in this paper, and applied to validation data for different genetically modified elements. Remarks were drawn on the analytical results. The fuzzy-logic based rules were shown to be applicable to improve interpretation of results and facilitate overall evaluation of the multiplex method.

  18. Computerized Segmentation and Characterization of Breast Lesions in Dynamic Contrast-Enhanced MR Images Using Fuzzy c-Means Clustering and Snake Algorithm

    Directory of Open Access Journals (Sweden)

    Yachun Pang

    2012-01-01

    Full Text Available This paper presents a novel two-step approach that incorporates fuzzy c-means (FCMs clustering and gradient vector flow (GVF snake algorithm for lesions contour segmentation on breast magnetic resonance imaging (BMRI. Manual delineation of the lesions by expert MR radiologists was taken as a reference standard in evaluating the computerized segmentation approach. The proposed algorithm was also compared with the FCMs clustering based method. With a database of 60 mass-like lesions (22 benign and 38 malignant cases, the proposed method demonstrated sufficiently good segmentation performance. The morphological and texture features were extracted and used to classify the benign and malignant lesions based on the proposed computerized segmentation contour and radiologists’ delineation, respectively. Features extracted by the computerized characterization method were employed to differentiate the lesions with an area under the receiver-operating characteristic curve (AUC of 0.968, in comparison with an AUC of 0.914 based on the features extracted from radiologists’ delineation. The proposed method in current study can assist radiologists to delineate and characterize BMRI lesion, such as quantifying morphological and texture features and improving the objectivity and efficiency of BMRI interpretation with a certain clinical value.

  19. Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment

    Institute of Scientific and Technical Information of China (English)

    ZOU Zhi-hong; YUN Yi; SUN Jing-nan

    2006-01-01

    Considering the difficulty of fuzzy synthetic evaluation method in calculation of the multiple factors and ignorance of the relationship among evaluating objects, a new weight evaluation process using entropy method was introduced. This improved method for determination of weight of the evaluating indicators was applied in water quality assessment of the Three Gorges reservoir area.The results showed that this method was favorable for fuzzy synthetic evaluation when there were more than one evaluating objects.One calculation was enough for calculating every monitoring point. Compared with the original evaluation method, the method predigested the fuzzy synthetic evaluation process greatly and the evaluation results are more reasonable.

  20. Research on Fuzzy Diagnosis Method of Boiler Steam and Water Pipe Leakage

    Science.gov (United States)

    Yin, Xianglei; Wang, Yan

    Diagnosis pipe leakage timely and accurately is of great significance for safe and economic operation for boilers. According to the characteristics of the failure of boiler, this paper gives new function to describe fault symptoms and puts forward a new method of fault fuzzy recognition. Through simulation experiment, the new method was validated and compared with the existing fault diagnosis methods. The simulation results show that the new method for boiler failure recognition has high accuracy, and is better than other methods.

  1. A HIERARCHICAL STRUCTURE FOR SHIP DIESEL ENGINE TROUBLE-SHOOTING PROBLEM USING FUZZY AHP AND FUZZY VIKOR HYBRID METHODS

    Directory of Open Access Journals (Sweden)

    Abit Balin

    2015-03-01

    Full Text Available Although considerable technical preventive measures have been taken in marine diesel engine and auxiliary systems, it is possible to observe unexpected faults in the course of the operating conditions. These faults can become so severe that they can cause losses which can be irreversible. This study aims to present Fuzzy Analytic Hierarchy Process (AHP and VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje methods applied for the expert failure detection of marine diesel engine and auxiliary systems. In this study, the failures of marine diesel engine have been revealed and prioritized. Accordingly, the section of the machine from which the failures primarily arise has been determined. At the same time, the importance of the effective use of time in determining and responding to the failures has been indicated. By means of the evaluation of decision-making groups, the system most severely affected by failures has been decided.

  2. FUZZY CLUSTERING ALGORITHMS FOR WEB PAGES AND CUSTOMER SEGMENTS%Web页面和客户群体的模糊聚类算法

    Institute of Scientific and Technical Information of China (English)

    宋擒豹; 沈钧毅

    2001-01-01

    Web log mining is broadly used in E-commerce and personalizationof the Web. In this paper, the fuzzy clustering algorithms for Web pages and customers is presented. First, the fuzzy sets of Web page and customer are setup separately according to the hitting information of customers. Second, the fuzzy similarity matrices ave constructed on the basis of the fuzzy sets and the Max-Min similarity measure scheme. Finally, Web page clusters and tustomer segments are abstracted directly from the corresponding fuzzy similarity matrix. Experiments show the effectiveness of the algorithm.%web日志挖掘在电子商务和个性化web等方面有着广泛的应用.文章介绍了一种web页面和客户群体的模糊聚类算法.在该算法中,首先根据客户对Web站点的浏览情况分别建立Web页面和客户的模糊集,在此基础上根据Max—Min模糊相似性度量规则构造相应的模糊相似矩阵,然后根据模糊相似矩阵直接进行聚类.实验结果表明该算法是有效的.

  3. A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering

    Science.gov (United States)

    Chahine, Firas Safwan

    2012-01-01

    Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…

  4. A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering

    Science.gov (United States)

    Chahine, Firas Safwan

    2012-01-01

    Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…

  5. AN INTELLIGENT NEURO-FUZZY TERMINAL SLIDING MODE CONTROL METHOD WITH APPLICATION TO ATOMIC FORCE MICROSCOPE

    Directory of Open Access Journals (Sweden)

    Seied Yasser Nikoo

    2016-11-01

    Full Text Available In this paper, a neuro-fuzzy fast terminal sliding mode control method is proposed for controlling a class of nonlinear systems with bounded uncertainties and disturbances. In this method, a nonlinear terminal sliding surface is firstly designed. Then, this sliding surface is considered as input for an adaptive neuro-fuzzy inference system which is the main controller. A proportinal-integral-derivative controller is also used to asist the neuro-fuzzy controller in order to improve the performance of the system at the begining stage of control operation. In addition, bee algorithm is used in this paper to update the weights of neuro-fuzzy system as well as the parameters of the proportinal-integral-derivative controller. The proposed control scheme is simulated for vibration control in a model of atomic force microscope system and the results are compared with conventional sliding mode controllers. The simulation results show that the chattering effect in the proposed controller is decreased in comparison with the sliding mode and the terminal sliding mode controllers. Also, the method provides the advantages of fast convergence and low model dependency compared to the conventional methods.

  6. Study on the accuracy of comprehensive evaluating method based on fuzzy set theory

    Institute of Scientific and Technical Information of China (English)

    Xu Weixiang; Liu Xumin

    2005-01-01

    The evaluation method and its accuracy for evaluating complex systems are considered. In order to evaluate accurately complex systems, the existed evaluating methods are simply analyzed, and a new comprehensive evaluating method is developed. The new method is integration of Delphi approach, analytic hierarchy process, gray interconnect degree and fuzzy evaluation (DHGF). Its theory foundation is the meta-synthesis methodology from qualitative analysis to quantitative analysis. According to fuzzy set approach, using the methods of concordance of evaluation, redundant verify, double models redundant, and limitations of the method etc, the accuracy of evaluating method of DHGF is estimated, and a practical example is given. The result shows that using the method to evaluate complex system projects is feasible and credible.

  7. AGENT-BASED SIMULATION FOR KANSEI ENGINEERING: TESTING A FUZZY LINEAR QUANTIFICATION METHOD IN AN ARTIFICIAL WORLD

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    This paper argues that agent-based simulation can be used as a way for testing Kansei Engineering methods which deal with the human reaction from sensory to mental state, that is, sensitivity, sense,sensibility, feeling, esthetics, emotion affection and intuition. A new fuzzy linear quantification method is tested in an artificial world by agent-based modeling and simulations, and the performance of the fuzzy linear method is compared with that of a genetic algorithm. The simulations can expand people's imagination and enhance people's intuition that the new fuzzy linear quantification method is effective.

  8. New Fuzzy-based Retinex Method for the Illumination Normalization of Face Recognition

    Directory of Open Access Journals (Sweden)

    Gi Pyo Nam

    2012-10-01

    Full Text Available We propose a new illumination normalization for face recognition which robust in relation to the illumination variations on mobile devices. This research is novel in the following five ways when compared to previous works: (i a new fuzzy‐based Retinex method is proposed for illumination normalization; (ii the performance of face recognition is enhanced by determining the optimal parameter of Retinex filtering based on fuzzy logic; (iii the output of the fuzzy membership function is adaptively determined based on the mean and standard deviations of the grey values of the detected face region; (iv through the comparison of various defuzzification methods in terms of the accuracy of face recognition, one optimal method was selected; (v we proved the validations of the proposed method by testing it with various face recognition methods. Experimental results showed that the accuracy of the face recognition with the proposed method was enhanced compared to previous ones.

  9. An Interval-Valued Intuitionistic Fuzzy TOPSIS Method Based on an Improved Score Function

    Directory of Open Access Journals (Sweden)

    Zhi-yong Bai

    2013-01-01

    Full Text Available This paper proposes an improved score function for the effective ranking order of interval-valued intuitionistic fuzzy sets (IVIFSs and an interval-valued intuitionistic fuzzy TOPSIS method based on the score function to solve multicriteria decision-making problems in which all the preference information provided by decision-makers is expressed as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by IVIFS value and the information about criterion weights is known. We apply the proposed score function to calculate the separation measures of each alternative from the positive and negative ideal solutions to determine the relative closeness coefficients. According to the values of the closeness coefficients, the alternatives can be ranked and the most desirable one(s can be selected in the decision-making process. Finally, two illustrative examples for multicriteria fuzzy decision-making problems of alternatives are used as a demonstration of the applications and the effectiveness of the proposed decision-making method.

  10. SPEED CONTROL OF PMBLDC DRIVE WITH GATE CONTROL METHOD USING CONVENTIONAL AND FUZZY CONTROLLER

    Directory of Open Access Journals (Sweden)

    T.V.NARMADHA,

    2010-11-01

    Full Text Available The paper presents simulation results of fuzzy logic and conventional proportional integral controller for the sensorless speed control of permanent magnet brushless dc (PMBLDC motor using Gate control method. Although conventional PI controllers are widely used in the industry due to its simple control structure and ease of implementation, these controllers pose difficulties under the conditions of nonlinearity, load disturbances and parametric variations. Moreover PI controllers require precise linear mathematical models. In the paper, the performance of the permanent magnet brushless dc motor drive is examined with the aid of the fuzzy logic controller. The fuzzy logic controller shows improved performance compared to the conventional PI speed controller. The module of the Three Phase inverter system controlled Permanent magnet Brushless DC motor is simulated using PI and Fuzzy Logic Controller and implemented in closed loop model. . By simulation, the characteristics of the PMBLDCM system are investigated. THD analysis for the methods is presented. The simulation results indicate FLC has improved performance.

  11. An improved method of fuzzy support degree based on uncertainty analysis

    Science.gov (United States)

    Huang, Yuan; Wu, Jing; Wu, Lihua; Sheng, Weidong

    2015-10-01

    Most multisensor association algorithms based on fuzzy set theory forms the opinion of fuzzy proposition using a simple triangular function. It does not take the randomness of measurements into account. Otherwise, the variance of sensors supposed to be known in the triangular function, but in fact the exact variance is difficult to acquire. This paper discuss about two situations with known and unknown variance of sensors. First, with known variance and known mean. This paper proposes a method, which use the probability ratio to calculate the fuzzy support degree. The interaction between the two objects is considered. Second, with unknown variance and known mean value, we replace the sample mean in the gray auto correlation function with the real sensor mean value to analysis the uncertainty which is the correlation coefficient between targets and measurements actually. In this way, it can deal with the case of small sample. Finally, form the opinion about the fuzzy proposition in terms of weighting the opinion of all the sensors based on the result of uncertainty analysis. Sufficient simulations on some typical scenarios are performed, and the results indicate that the method presented is efficient.

  12. Hesitant Fuzzy Thermodynamic Method for Emergency Decision Making Based on Prospect Theory.

    Science.gov (United States)

    Ren, Peijia; Xu, Zeshui; Hao, Zhinan

    2016-12-30

    Due to the timeliness of emergency response and much unknown information in emergency situations, this paper proposes a method to deal with the emergency decision making, which can comprehensively reflect the emergency decision making process. By utilizing the hesitant fuzzy elements to represent the fuzziness of the objects and the hesitant thought of the experts, this paper introduces the negative exponential function into the prospect theory so as to portray the psychological behaviors of the experts, which transforms the hesitant fuzzy decision matrix into the hesitant fuzzy prospect decision matrix (HFPDM) according to the expectation-levels. Then, this paper applies the energy and the entropy in thermodynamics to take the quantity and the quality of the decision values into account, and defines the thermodynamic decision making parameters based on the HFPDM. Accordingly, a whole procedure for emergency decision making is conducted. What is more, some experiments are designed to demonstrate and improve the validation of the emergency decision making procedure. Last but not the least, this paper makes a case study about the emergency decision making in the firing and exploding at Port Group in Tianjin Binhai New Area, which manifests the effectiveness and practicability of the proposed method.

  13. Hydrogen Production Technologies Evaluation Based on Interval-Valued Intuitionistic Fuzzy Multiattribute Decision Making Method

    Directory of Open Access Journals (Sweden)

    Dejian Yu

    2014-01-01

    Full Text Available We establish a decision making model for evaluating hydrogen production technologies in China, based on interval-valued intuitionistic fuzzy set theory. First of all, we propose a series of interaction interval-valued intuitionistic fuzzy aggregation operators comparing them with some widely used and cited aggregation operators. In particular, we focus on the key issue of the relationships between the proposed operators and existing operators for clear understanding of the motivation for proposing these interaction operators. This research then studies a group decision making method for determining the best hydrogen production technologies using interval-valued intuitionistic fuzzy approach. The research results of this paper are more scientific for two reasons. First, the interval-valued intuitionistic fuzzy approach applied in this paper is more suitable than other approaches regarding the expression of the decision maker’s preference information. Second, the results are obtained by the interaction between the membership degree interval and the nonmembership degree interval. Additionally, we apply this approach to evaluate the hydrogen production technologies in China and compare it with other methods.

  14. Medical Waste Disposal Method Selection Based on a Hierarchical Decision Model with Intuitionistic Fuzzy Relations

    Directory of Open Access Journals (Sweden)

    Wuyong Qian

    2016-09-01

    Full Text Available Although medical waste usually accounts for a small fraction of urban municipal waste, its proper disposal has been a challenging issue as it often contains infectious, radioactive, or hazardous waste. This article proposes a two-level hierarchical multicriteria decision model to address medical waste disposal method selection (MWDMS, where disposal methods are assessed against different criteria as intuitionistic fuzzy preference relations and criteria weights are furnished as real values. This paper first introduces new operations for a special class of intuitionistic fuzzy values, whose membership and non-membership information is cross ratio based ]0, 1[-values. New score and accuracy functions are defined in order to develop a comparison approach for ]0, 1[-valued intuitionistic fuzzy numbers. A weighted geometric operator is then put forward to aggregate a collection of ]0, 1[-valued intuitionistic fuzzy values. Similar to Saaty’s 1–9 scale, this paper proposes a cross-ratio-based bipolar 0.1–0.9 scale to characterize pairwise comparison results. Subsequently, a two-level hierarchical structure is formulated to handle multicriteria decision problems with intuitionistic preference relations. Finally, the proposed decision framework is applied to MWDMS to illustrate its feasibility and effectiveness.

  15. Application of fuzzy inference system by Sugeno method on estimating of salt production

    Science.gov (United States)

    Yulianto, Tony; Komariyah, Siti; Ulfaniyah, Nurita

    2017-08-01

    Salt is one of the most important needs in everyday life. Making traditional salt largely is done by smallholder farmers in addition by manufacturers of industrial salt. factors that affect the production of salt include seawater, soil, water influence and weather conditions including rainfall wind speed and solar radiation or long dry erratic, these conditions obviously affect the salt farmers that will affect the production quantities of salt produced by salt farmers. In this study, the fuzzy logic method is applied to Sugeno fuzzy inference systems to estimate the production of salt by variables - variables that affect it. This study aims to estimate how much production by applying fuzzy inference systems zero-order Sugeno method based on the variable wind speed, solar radiation, rainfall and the amount of production. Retrieval of data obtained from the Air Quality Meteorology and Geophysics. salt farmers in Pamekasan District of Pademawu Village Majungan. Data taken within 2 years per week from June to December of 2014 and 2015. The Sugeno fuzzy logic model in this study using output (consequent) in the form of equation constants (Sugeno models Order zero). Apparently from the research results obtained by the error value most low at 0.0917, so it can be said to be close to zero.

  16. Medical Waste Disposal Method Selection Based on a Hierarchical Decision Model with Intuitionistic Fuzzy Relations.

    Science.gov (United States)

    Qian, Wuyong; Wang, Zhou-Jing; Li, Kevin W

    2016-09-09

    Although medical waste usually accounts for a small fraction of urban municipal waste, its proper disposal has been a challenging issue as it often contains infectious, radioactive, or hazardous waste. This article proposes a two-level hierarchical multicriteria decision model to address medical waste disposal method selection (MWDMS), where disposal methods are assessed against different criteria as intuitionistic fuzzy preference relations and criteria weights are furnished as real values. This paper first introduces new operations for a special class of intuitionistic fuzzy values, whose membership and non-membership information is cross ratio based ]0, 1[-values. New score and accuracy functions are defined in order to develop a comparison approach for ]0, 1[-valued intuitionistic fuzzy numbers. A weighted geometric operator is then put forward to aggregate a collection of ]0, 1[-valued intuitionistic fuzzy values. Similar to Saaty's 1-9 scale, this paper proposes a cross-ratio-based bipolar 0.1-0.9 scale to characterize pairwise comparison results. Subsequently, a two-level hierarchical structure is formulated to handle multicriteria decision problems with intuitionistic preference relations. Finally, the proposed decision framework is applied to MWDMS to illustrate its feasibility and effectiveness.

  17. A Literature Review Fuzzy Pay-Off-Method – A Modern Approach in Valuation

    Directory of Open Access Journals (Sweden)

    Daniel Manaţe

    2015-01-01

    Full Text Available This article proposes to present a modern approach in the analysis of updated cash flows. The approach is based on the Fuzzy Pay-Off-Method (FPOM for Real Option Valuation (ROV. This article describes a few types of models for the valuation of real options currently in use. In support for the chosen FPOM method, we included the mathematical model that stands at the basis of this method and a case study.

  18. Decision making with fuzzy probability assessments and fuzzy payoff

    Institute of Scientific and Technical Information of China (English)

    Song Yexin; Yin Di; Chen Mianyun

    2005-01-01

    A novel method for decision making with fuzzy probability assessments and fuzzy payoff is presented. The consistency of the fuzzy probability assessment is considered. A fuzzy aggregate algorithm is used to indicate the fuzzy expected payoff of alternatives. The level sets of each fuzzy expected payoff are then obtained by solving linear programming models. Based on a defuzzification function associated with the level sets of fuzzy number and a numerical integration formula (Newton-Cotes formula), an effective approach to rank the fuzzy expected payoff of alternatives is also developed to determine the best alternative. Finally, a numerical example is provided to illustrate the proposed method.

  19. Fuzzy logic in management

    CERN Document Server

    Carlsson, Christer; Fullér, Robert

    2004-01-01

    Fuzzy Logic in Management demonstrates that difficult problems and changes in the management environment can be more easily handled by bringing fuzzy logic into the practice of management. This explicit theme is developed through the book as follows: Chapter 1, "Management and Intelligent Support Technologies", is a short survey of management leadership and what can be gained from support technologies. Chapter 2, "Fuzzy Sets and Fuzzy Logic", provides a short introduction to fuzzy sets, fuzzy relations, the extension principle, fuzzy implications and linguistic variables. Chapter 3, "Group Decision Support Systems", deals with group decision making, and discusses methods for supporting the consensus reaching processes. Chapter 4, "Fuzzy Real Options for Strategic Planning", summarizes research where the fuzzy real options theory was implemented as a series of models. These models were thoroughly tested on a number of real life investments, and validated in 2001. Chapter 5, "Soft Computing Methods for Reducing...

  20. 关于切换回归的集成模糊聚类算法 GFC%An Integrated Fuzzy Clustering Algorithm GFC for Switching Regressions

    Institute of Scientific and Technical Information of China (English)

    王士同; 江海峰; 陆宏钧

    2002-01-01

    已经有多个方法可用于解决切换回归问题.根据所提出的基于Newton引力定理的引力聚类算法GC,结合模糊聚类算法,进一步提出了新的集成模糊聚类算法 GFC.理论分析表明GFC 能收敛到局部最小.实验结果表明GFC在解决切换回归问题时,比标准模糊聚类算法更有效,特别在收敛速度方面.%In order to solve switching regression problems, many approaches have been investigated. In this paper, anintegrated fuzzy clustering algorithm GFC that combines gravity-based clustering algorithm GC with fuzzy clustering is presented. GC, as a new hard clustering algorithm presented here, is based on the well-known Newton's Gravity Law. The theoretic analysis shows that GFC can conve rge to a local minimum of the object function. Experimental results show that GFC for switching regression problems has better performance than standard fuzzy clustering algorithms, especially in terms of convergence speed.